ORCID Profile
0000-0003-1395-261X
Current Organisation
University of Queensland
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Systems | Database Management | Pattern Recognition and Data Mining | Research, Science and Technology Policy | Information Systems Development Methodologies | Business Information Systems | Global Information Systems | Interorganisational Information Systems and Web Services |
ANZSRC Socio-Economic Objective (SEO)
Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Electronic Information Storage and Retrieval Services | Expanding Knowledge in the Information and Computing Sciences | Application Software Packages (excl. Computer Games) | Technological and Organisational Innovation | Expanding Knowledge in Technology
Related Links
Publications
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
Publisher: IEEE
Date: 04-2020
Dynamic User Modeling in Social Media Systems
Publisher: Association for Computing Machinery (ACM)
Date: 09-03-2015
DOI: 10.1145/2699670
Abstract: Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top- k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.
A block-based generative model for attributed network embedding
Publisher: Springer Science and Business Media LLC
Date: 15-07-2021
DDHH: A Decentralized Deep Learning Framework for Large-scale Heterogeneous Networks
Publisher: IEEE
Date: 04-2021
Continuous Input Embedding Size Search For Recommender Systems
Publisher: ACM
Date: 18-07-2023
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 17-07-2019
DOI: 10.1609/AAAI.V33I01.330110031
Abstract: Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cross-task features to enhance the task performance. In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on SCT considered various semantic information, such as sentiment and topic, but lack the logic information between sentences which is an essential element of stories. Thus we propose to extract the logic information during the course of the story to improve the understanding of the whole story. The logic information is modeled with the help of the NLI task. Experimental results prove the strength of the logic information.
DeHIN: A Decentralized Framework for Embedding Large-Scale Heterogeneous Information Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Towards Revenue Maximization with Popular and Profitable Products
Publisher: Association for Computing Machinery (ACM)
Date: 30-11-2021
DOI: 10.1145/3488058
Abstract: Economic-wise, a common goal for companies conducting marketing is to maximize the return revenue rofit by utilizing the various effective marketing strategies. Consumer behavior is crucially important in economy and targeted marketing, in which behavioral economics can provide valuable insights to identify the biases and profit from customers. Finding credible and reliable information on products’ profitability is, however, quite difficult since most products tend to peak at certain times w.r.t. seasonal sales cycles in a year. On-Shelf Availability (OSA) plays a key factor for performance evaluation. Besides, staying ahead of hot product trends means we can increase marketing efforts without selling out the inventory. To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the O n-shelf P opular and most P rofitable P roducts (OPPPs) for the targeted marketing. To tackle the revenue maximization problem, we model the k-satisfiable product concept and propose an algorithmic framework for searching OPPP and its variants. Extensive experiments are conducted on several real-world datasets to evaluate the effectiveness and efficiency of the proposed algorithm.
Unified User and Item Representation Learning for Joint Recommendation in Social Network
Publisher: Springer International Publishing
Date: 2018
Extracting representative user subset of social networks towards user characteristics and topological features
Publisher: Springer Science and Business Media LLC
Date: 30-06-2020
Monte Carlo sampling in diffusive dynamical systems
Publisher: AIP Publishing
Date: 05-2018
DOI: 10.1063/1.5025627
Abstract: We introduce a Monte Carlo algorithm to efficiently compute transport properties of chaotic dynamical systems. Our method exploits the importance s ling technique that favors trajectories in the tail of the distribution of displacements, where deviations from a diffusive process are most prominent. We search for initial conditions using a proposal that correlates states in the Markov chain constructed via a Metropolis-Hastings algorithm. We show that our method outperforms the direct s ling method and also Metropolis-Hastings methods with alternative proposals. We test our general method through numerical simulations in 1D (box-map) and 2D (Lorentz gas) systems.
Geographical Constraint and Temporal Similarity Modeling for Point-of-Interest Recommendation
Publisher: No publisher found
Date: 2015
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Publisher: IEEE
Date: 12-2021
Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 10-10-2016
DOI: 10.1145/2873055
Abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM) , to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the erse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top- k recommendations. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.
Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1
Publisher: Association for Computing Machinery (ACM)
Date: 07-02-2023
DOI: 10.1145/3579995
Who are the Best Adopters? User Selection Model for Free Trial Item Promotion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Enhancing Social Recommendation With Adversarial Graph Convolutional Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Publisher: arXiv
Date: 2022
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
Publisher: arXiv
Date: 2022
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Publisher: ACM
Date: 21-10-2023
Group-level personality detection based on text generated networks
Publisher: Springer Science and Business Media LLC
Date: 21-09-2019
Are Graph Augmentations Necessary?
Publisher: ACM
Date: 06-07-2022
Heterogeneous Hypergraph Embedding for Graph Classification
Publisher: ACM
Date: 08-03-2021
Learning Graph-based POI Embedding for Location-based Recommendation
Publisher: ACM
Date: 24-10-2016
Publishing Graph Node Strength Histogram with Edge Differential Privacy
Publisher: Springer International Publishing
Date: 2018
Modeling Patient Visit Using Electronic Medical Records for Cost Profile Estimation
Publisher: Springer International Publishing
Date: 2018
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Publisher: ACM
Date: 18-07-2023
TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction.
Publisher: IEEE
Date: 11-2018
Effective and Efficient User Account Linkage across Location Based Social Networks
Publisher: IEEE
Date: 04-2018
Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Publisher: arXiv
Date: 2022
SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud
Publisher: Springer Science and Business Media LLC
Date: 2020
An Efficient Framework for Multiple Subgraph Pattern Matching Models
Publisher: Springer Science and Business Media LLC
Date: 11-2019
Challenging the Long Tail Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 05-2012
Abstract: The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been largely attributed to a "long tail" phenomenon. Although the majority of their inventory is not in high demand, these niche products, unavailable at limited-inventory competitors, generate a significant fraction of total revenue in aggregate. In addition, tail product availability can boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche tastes. However, most of existing recommender systems, especially collaborative filter based methods, can not recommend tail products due to the data sparsity issue. It has been widely acknowledged that to recommend popular products is easier yet more trivial while to recommend long tail products adds more novelty yet it is also a more challenging task. In this paper, we propose a novel suite of graph-based algorithms for the long tail recommendation. We first represent user-item information with undirected edge-weighted graph and investigate the theoretical foundation of applying Hitting Time algorithm for long tail item recommendation. To improve recommendation ersity and accuracy, we extend Hitting Time and propose efficient Absorbing Time algorithm to help users find their favorite long tail items. Finally, we refine the Absorbing Time algorithm and propose two entropy-biased Absorbing Cost algorithms to distinguish the variation on different user-item rating pairs, which further enhances the effectiveness of long tail recommendation. Empirical experiments on two real life datasets show that our proposed algorithms are effective to recommend long tail items and outperform state-of-the-art recommendation techniques.
A Time and Sentiment Unification Model for Personalized Recommendation
Publisher: No publisher found
Date: 2017
Graph Embeddings for One-pass Processing of Heterogeneous Queries
Publisher: IEEE
Date: 04-2020
Predicting Users' Purchasing Behaviors Using Their Browsing History
Publisher: No publisher found
Date: 2015
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion
Publisher: ACM
Date: 11-02-2022
Time-Constrained Graph Pattern Matching in a Large Temporal Graph
Publisher: Springer International Publishing
Date: 2017
Induction and organization of Ca2+ waves by enteric neural reflexes
Publisher: Springer Science and Business Media LLC
Date: 05-1999
DOI: 10.1038/19973
Abstract: The motility of the gastrointestinal tract consists of local, non-propulsive mixing (pendular or segmental) and propulsive (peristaltic) movements. It is generally considered that mixing movements are produced by intrinsic pacemakers which generate rhythmic contractions, and peristalsis by intrinsic excitatory and inhibitory neural reflex pathways, but the relationship between mixing and peristalsis is poorly understood. Peristalsis is compromised in mice lacking interstitial cells of Cajal, suggesting that these pacemaker cells may also be involved in neural reflexes. Here we show that mixing movements within longitudinal muscle result from spontaneously generated waves of elevated internal calcium concentration which originate from discrete locations (pacing sites), spread with anisotropic conduction velocities in al directions, and terminate by colliding with each other or with adjacent neurally suppressed regions. Excitatory neural reflexes control the spread of excitability by inducing new pacing sites and enhancing the overall frequency of pacing, whereas inhibitory reflexes suppress the ability of calcium waves to propagate. We provide evidence that the enteric nervous system organizes mixing movements to generate peristalsis, linking the neural regulation of pacemakers to both types of gut motility.
Inferring Substitutable Products with Deep Network Embedding
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.
Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Discrete Deep Learning for Fast Content-Aware Recommendation
Publisher: ACM
Date: 02-02-2018
Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs
Publisher: Springer International Publishing
Date: 2018
Computing Crowd Consensus with Partial Agreement
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Answer validation for generic crowdsourcing tasks with minimal efforts
Publisher: Springer Science and Business Media LLC
Date: 13-10-2017
Social Boosted Recommendation With Folded Bipartite Network Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Spatial-Temporal Meta-path Guided Explainable Crime Prediction
Publisher: arXiv
Date: 2022
EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviors
Publisher: No publisher found
Date: 2020
Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Publisher: arXiv
Date: 2022
Scalable maximal subgraph mining with backbone-preserving graph convolutions
Publisher: Elsevier BV
Date: 10-2023
Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system
Publisher: Elsevier BV
Date: 10-2018
Detecting Rumours with Latency Guarantees using Massive Streaming Data
Publisher: arXiv
Date: 2022
Matching user accounts based on user generated content across social networks
Publisher: Elsevier BV
Date: 06-2018
Streaming ranking based recommender systems
Publisher: ACM
Date: 27-06-2018
Preface
Publisher: Springer Science and Business Media LLC
Date: 07-2018
Graph-Based Metric Embedding for Next POI Recommendation
Publisher: No publisher found
Date: 2016
Restricted boltzmann machine based active learning for sparse recommendation
Publisher: Springer International Publishing
Date: 2018
TPM
Publisher: Association for Computing Machinery (ACM)
Date: 11-2018
DOI: 10.1145/3230706
Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low s ling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model ( TPM ), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users’ spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top- k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.
Layered convolutional dictionary learning for sparse coding itemsets
Publisher: Springer Science and Business Media LLC
Date: 11-05-2019
A temporal context-aware model for user behavior modeling in social media systems
Publisher: ACM
Date: 18-06-2014
Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs
Publisher: No publisher found
Date: 2018
Extracting Representative User Subset of Social Networks Towards User Characteristics and Topological Features
Publisher: No publisher found
Date: 2018
Neural Pairwise Ranking Factorization Machine for Item Recommendation
Publisher: No publisher found
Date: 2020
Online sales prediction via trend alignment-based multitask recurrent neural networks
Publisher: Springer Science and Business Media LLC
Date: 09-10-2019
Decentralized Collaborative Learning Framework for Next POI Recommendation
Publisher: arXiv
Date: 2022
Spatiotemporal Recommendation in Geo-Social Networks
Publisher: No publisher found
Date: 2018
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Publisher: Springer International Publishing
Date: 2021
Semi-decentralized Federated Ego Graph Learning for Recommendation
Publisher: ACM
Date: 30-04-2023
Preface - Special Issue on Misinformation on the Web
Publisher: Elsevier BV
Date: 2022
Spatiotemporal Representation Learning for Translation-Based POI Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 27-01-2019
DOI: 10.1145/3295499
Abstract: The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a time, location pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
LCARS: a location-content-aware recommender system
Publisher: ACM
Date: 11-08-2013
Lightweight Self-Attentive Sequential Recommendation
Publisher: ACM
Date: 26-10-2021
Graph-Based Metric Embedding for Next POI Recommendation
Publisher: Springer International Publishing
Date: 2016
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
Publisher: ACM
Date: 27-02-2023
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
Publisher: ACM
Date: 18-07-2023
International Workshop on Privacy, Security and Trust in Computational Intelligence (PSTCI2021)
Publisher: ACM
Date: 26-10-2021
Decentralized Embedding Framework for Large-Scale Networks
Publisher: No publisher found
Date: 2020
CIRCLE: Continual Repair across Programming Languages
Publisher: arXiv
Date: 2022
Multiscale Adaptive Multifractal Detrended Fluctuation Analysis-Based Source Identification of Synchrophasor Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Leveraging multi-aspect time-related influence in location recommendation
Publisher: Springer Science and Business Media LLC
Date: 02-05-2018
Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation
Publisher: ACM
Date: 11-07-2021
Privacy Protection in Deep Multi-modal Retrieval
Publisher: ACM
Date: 11-07-2021
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Variational Counterfactual Prediction under Runtime Domain Corruption
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publishing Graph Node Strength Histogram with Edge Differential Privacy
Publisher: No publisher found
Date: 2018
A Privacy-Preserving Framework for Subgraph Pattern Matching in Cloud
Publisher: No publisher found
Date: 2018
Where are we in embedding spaces?
Publisher: ACM
Date: 14-08-2021
TeRec: A Temporal Recommender System Over Tweet Stream
Publisher: Association for Computing Machinery (ACM)
Date: 08-2013
Abstract: As social media further integrates into our daily lives, people are increasingly immersed in real-time social streams via services such as Twitter and Weibo. One important observation in these online social platforms is that users' interests and the popularity of topics shift very fast, which poses great challenges on existing recommender systems to provide the right topics at the right time. In this paper, we extend the online ranking technique and propose a temporal recommender system - TeRec. In TeRec, when posting tweets, users can get recommendations of topics (hashtags) according to their real-time interests, they can also generate fast feedbacks according to the recommendations. TeRec provides the browser-based client interface which enables the users to access the real time topic recommendations, and the server side processes and stores the real-time stream data. The experimental study demonstrates the superiority of TeRec in terms of temporal recommendation accuracy.
Exploiting Reshaping Subgraphs from Bilateral Propagation Graphs
Publisher: No publisher found
Date: 2018
Cluster query: a new query pattern on temporal knowledge graph
Publisher: Springer Science and Business Media LLC
Date: 13-02-2020
Learning to Ask Appropriate Questions in Conversational Recommendation
Publisher: ACM
Date: 11-07-2021
LECF: recommendation via learnable edge collaborative filtering
Publisher: Springer Science and Business Media LLC
Date: 23-12-2022
Learning to Distill Graph Neural Networks
Publisher: ACM
Date: 27-02-2023
Influenced Nodes Discovery in Temporal Contact Network
Publisher: Springer International Publishing
Date: 2017
Temporal Meta-path Guided Explainable Recommendation
Publisher: ACM
Date: 08-03-2021
Modeling Patient Visit Using Electronic Medical Records for Cost Profile Estimation
Publisher: No publisher found
Date: 2018
Preference-aware task assignment in spatial crowdsourcing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Retaining Data from Streams of Social Platforms with Minimal Regret
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: Today's social platforms, such as Twitter and Facebook, continuously generate massive volumes of data. The resulting data streams exceed any reasonable limit for permanent storage, especially since data is often redundant, overlapping, sparse, and generally of low value. This calls for means to retain solely a small fraction of the data in an online manner. In this paper, we propose techniques to effectively decide which data to retain, such that the induced loss of information, the regret of neglecting certain data, is minimized. These techniques enable not only efficient processing of massive streaming data, but are also adaptive and address the dynamic nature of social media. Experiments on large-scale real-world datasets illustrate the feasibility of our approach in terms of both, runtime and information quality.
Time-Constrained Graph Pattern Matching in a Large Temporal Graph
Publisher: No publisher found
Date: 2017
Discovering organized POI groups in a city
Publisher: Springer International Publishing
Date: 2015
Efficient Streaming Subgraph Isomorphism with Graph Neural Networks
Publisher: Association for Computing Machinery (ACM)
Date: 2021
Abstract: Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do not scale to a dynamic setting of a continuous stream of queries. In this paper, we address the scalability challenges induced by a stream of subgraph isomorphism queries by caching and re-use of previous results. We first present a novel subgraph index based on graph embeddings that serves as the foundation for efficient stream processing. It enables not only effective caching and re-use of results, but also speeds-up traditional algorithms for subgraph isomorphism in case of cache misses. Moreover, we propose cache management policies that incorporate notions of reusability of query results. Experiments using real-world datasets demonstrate the effectiveness of our approach in handling isomorphic subgraph search for streams of queries.
Mobi-SAGE: A Sparse Additive Generative Model for Mobile App Recommendation
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICDE.2017.43
TinyAD: Memory-Efficient Anomaly Detection for Time Series Data in Industrial IoT
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 27-09-2021
DOI: 10.1145/3466641
Abstract: With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Publisher: arXiv
Date: 2022
Self-Supervised Graph Co-Training for Session-based Recommendation
Publisher: ACM
Date: 26-10-2021
A Unified Framework for Fine-Grained Opinion Mining from Online Reviews
Publisher: IEEE
Date: 2016
Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
SPTF: A Scalable Probabilistic Tensor Factorization Model for Semantic-Aware Behavior Prediction
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.68
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
Publisher: No publisher found
Date: 2019
Spatio-Temporal Recommendation in Social Media
Publisher: Springer Singapore
Date: 2016
Heterogeneous Collaborative Learning for Personalized Healthcare Analytics Via Messenger Distillation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
Publisher: ACM
Date: 21-10-2023
Expert team finding for review assignment
Publisher: IEEE
Date: 11-2016
Self-Supervised Learning for Recommender Systems: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
CRSAL
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2020
DOI: 10.1145/3394592
Abstract: Recommender systems have been attracting much attention from both academia and industry because of their ability to capture user interests and generate personalized item recommendations. As the life pace in contemporary society speeds up, traditional recommender systems are inevitably limited by their disconnected interaction styles and low adaptivity to users’ evolving demands. Consequently, conversational recommender systems emerge as a prospective research area, where an intelligent dialogue agent is integrated with a recommender system. Conversational recommender systems possess the ability to accurately understand end-users’ intent or request and generate human-like dialogue responses when performing recommendations. However, existing conversational recommender systems only allow the systems to ask users for more preference information, while users’ further questions and concerns about the recommended items (e.g., enquiring the location of a recommended restaurant) can hardly be addressed. Though the recent task-oriented dialogue systems allow for two-way communications, they are not easy to train because of their high dependence on human guidance in terms of user intent recognition and system response generation. Hence, to enable two-way human-machine communications and tackle the challenges brought by manually crafted rules, we propose Conversational Recommender System with Adversarial Learning (CRSAL), a novel end-to-end system to tackle the task of conversational recommendation. In CRSAL, we innovatively design a fully statistical dialogue state tracker coupled with a neural policy agent to precisely capture each user’s intent from limited dialogue data and generate conversational recommendation actions. We further develop an adversarial Actor-Critic reinforcement learning approach to adaptively refine the quality of generated system actions, thus ensuring coherent human-like dialogue responses. Extensive experiments on two benchmark datasets fully demonstrate the superiority of CRSAL on conversational recommendation tasks.
Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Social Influence-Based Group Representation Learning for Group Recommendation
Publisher: IEEE
Date: 04-2019
Exploiting Centrality Information with Graph Convolutions for Network Representation Learning
Publisher: IEEE
Date: 04-2019
LCARS: A Spatial Item Recommender System
Publisher: Association for Computing Machinery (ACM)
Date: 06-2014
DOI: 10.1145/2629461
Abstract: Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based recommender systems. The problem becomes even more challenging when people travel to a new city where they have no activity information. In this article, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants and shopping malls) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each in idual user and the local preference of each in idual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part takes a querying user along with a querying city as input, and automatically combines the learned interest of the querying user and the local preference of the querying city to produce the top- k recommendations. To speed up the online process, a scalable query processing technique is developed by extending both the Threshold Algorithm (TA) and TA-approximation algorithm. We evaluate the performance of our recommender system on two real datasets, that is, DoubanEvent and Foursquare, and one large-scale synthetic dataset. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency. Besides, the experimental analysis results also demonstrate the excellent interpretability of LCARS.
Hyperbolic Personalized Tag Recommendation
Publisher: No publisher found
Date: 2022
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs
Publisher: ACM
Date: 25-04-2022
Multi-hop path queries over knowledge graphs with neural memory networks
Publisher: Springer International Publishing
Date: 2019
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
Reliable Recommendation with Review-level Explanations
Publisher: IEEE
Date: 04-2021
Using Detected Visual Objects to Index Video Database
Publisher: No publisher found
Date: 2016
CIRCLE: continual repair across programming languages
Publisher: ACM
Date: 18-07-2022
Simplifying Graph-based Collaborative Filtering for Recommendation
Publisher: ACM
Date: 27-02-2023
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Publisher: arXiv
Date: 2022
Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation
Publisher: ACM
Date: 17-10-2018
Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks
Publisher: IEEE
Date: 04-2020
Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation
Publisher: Springer International Publishing
Date: 2017
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Publisher: ACM
Date: 18-07-2023
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Publisher: ACM
Date: 03-11-2019
Group Recommendation with Latent Voting Mechanism
Publisher: IEEE
Date: 04-2020
Single-shot Embedding Dimension Search in Recommender System
Publisher: arXiv
Date: 2022
Overcoming Data Sparsity in Group Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
TSAUB: A temporal-sentiment-aware user behavior model for personalized recommendation
Publisher: Springer International Publishing
Date: 2018
Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)
Publisher: IEEE
Date: 04-2021
FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort
Publisher: ACM
Date: 25-07-2020
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Publisher: ACM
Date: 08-03-2021
Multi-hop Path Queries over Knowledge Graphs with Neural Memory Networks
Publisher: No publisher found
Date: 2019
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices
Publisher: ACM
Date: 20-04-2020
Modeling location-based user rating profiles for personalized recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 04-2015
DOI: 10.1145/2663356
Abstract: This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top- k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top- k recommendation and the cold start problem.
Discovering interpretable geo-social communities for user behavior prediction
Publisher: IEEE
Date: 05-2016
Joint Event-Partner Recommendation in Event-Based Social Networks
Publisher: IEEE
Date: 04-2018
Semi-supervised Clustering with Deep Metric Learning
Publisher: No publisher found
Date: 2019
Multi-Hop Knowledge Graph Reasoning in Few-Shot Scenarios
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
SPORE: A sequential personalized spatial item recommender system
Publisher: IEEE
Date: 05-2016
Federated Unlearning for On-Device Recommendation
Publisher: ACM
Date: 27-02-2023
Predicting Passengers in Public Transportation Using Smart Card Data
Publisher: Springer International Publishing
Date: 2015
An integrated model based on deep multimodal and rank learning for point-of-interest recommendation
Publisher: Springer Science and Business Media LLC
Date: 03-2021
AIR: Attentional Intention-Aware Recommender Systems
Publisher: IEEE
Date: 04-2019
A unified model for stable and temporal topic detection from social media data
Publisher: IEEE
Date: 04-2013
A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs
Publisher: Association for Computing Machinery (ACM)
Date: 25-07-2016
DOI: 10.1145/2905373
Abstract: Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.
Hyperbolic Personalized Tag Recommendation
Publisher: Springer International Publishing
Date: 2022
A privacy-preserving framework for subgraph pattern matching in cloud
Publisher: Springer International Publishing
Date: 2018
Matching user accounts across social networks based on username and display name
Publisher: Springer Science and Business Media LLC
Date: 26-04-2019
Unified Question Generation with Continual Lifelong Learning
Publisher: ACM
Date: 25-04-2022
Exploiting Reshaping Subgraphs from Bilateral Propagation Graphs
Publisher: Springer International Publishing
Date: 2018
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Publisher: ACM
Date: 17-10-2021
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Shared-account Cross-domain Sequential Recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.
Learning Elastic Embeddings for Customizing On-Device Recommenders
Publisher: ACM
Date: 14-08-2021
Predicting Passengers in Public Transportation Using Smart Card Data
Publisher: No publisher found
Date: 2015
Fast-adapting and privacy-preserving federated recommender system
Publisher: Springer Science and Business Media LLC
Date: 03-10-2021
Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Publisher: IEEE
Date: 11-2019
Socially-Aware Self-Supervised Tri-Training for Recommendation
Publisher: ACM
Date: 14-08-2021
Towards the Learning of Weighted Multi-label Associative Classifiers
Publisher: IEEE
Date: 07-2018
ST-SAGE
Publisher: Association for Computing Machinery (ACM)
Date: 20-04-2017
DOI: 10.1145/3011019
Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid . To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.
Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection
Publisher: ACM
Date: 19-04-2021
Unified User and Item Representation Learning for Joint Recommendation in Social Network
Publisher: No publisher found
Date: 2018
Reinforced KGs reasoning for explainable sequential recommendation
Publisher: Springer Science and Business Media LLC
Date: 16-06-2021
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation
Publisher: Springer International Publishing
Date: 2019
LSIF: A System for Large-Scale Information Flow Detection Based on Topic-Related Semantic Similarity Measurement
Publisher: IEEE
Date: 12-2015
Local Variational Feature-Based Similarity Models for Recommending Top- N New Items
Publisher: Association for Computing Machinery (ACM)
Date: 11-02-2020
DOI: 10.1145/3372154
Abstract: The top- N recommendation problem has been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user’s preferences by estimating similarities between a target and user-rated items. Top- N recommendation remains a challenging task in scenarios where there is a lack of preference history for new items. Feature-based Similarity Models (FSMs) address this particular problem by extending item-based collaborative filtering by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSMs only estimate global similarity functions i.e., they estimate using preference information across all users. Moreover, the estimated similarity functions are linear hence, they may fail to capture the complex structure underlying item features. In this article, we propose to improve FSMs by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global preference patterns, we extend the global similarity function from linear to nonlinear, based on the effectiveness of variational autoencoders. We propose a Bayesian generative model, called the Local Variational Feature-based Similarity Model, to encapsulate local and global similarity functions. We present a variational Expectation Minimization algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Publisher: IEEE
Date: 05-2022
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning (Extended Abstract)
Publisher: IEEE
Date: 05-2022
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Publisher: ACM
Date: 06-07-2022
Decentralized Collaborative Learning Framework for Next POI Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 07-02-2023
DOI: 10.1145/3555374
Abstract: Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel d ecentralized c ollaborative l earning framework for POI r ecommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models’ dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.
Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 08-09-2021
DOI: 10.1145/3457949
Abstract: Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of in iduals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.
User guidance for efficient fact checking
Publisher: Association for Computing Machinery (ACM)
Date: 04-2019
Abstract: The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation. Construction and maintenance of a knowledge base thus has to rely on fact checking, an assessment of the credibility of facts. Due to an inherent lack of ground truth information, such fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a comprehensive framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Our framework is grounded in a novel probabilistic model that combines user input with automated credibility inference. Based thereon, we show how to guide users in fact checking by identifying the facts for which validation is most beneficial. Moreover, our framework includes techniques to reduce the manual effort invested in fact checking by determining when to stop the validation and by supporting efficient batching strategies. We further show how to handle fact checking in a streaming setting. Our experiments with three real-world datasets demonstrate the efficiency and effectiveness of our framework: A knowledge base of high quality, with a precision of above 90%, is constructed with only a half of the validation effort required by baseline techniques.
Neural Memory Streaming Recommender Networks with Adversarial Training
Publisher: ACM
Date: 19-07-2018
Utility Mining Across Multi-Dimensional Sequences
Publisher: Association for Computing Machinery (ACM)
Date: 10-05-2021
DOI: 10.1145/3446938
Abstract: Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, and profit), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this article, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract underline M /underline ulti- underline D /underline imensional underline U /underline tility-oriented underline S /underline equential useful patterns. To the best of our knowledge, this is the first study that incorporates the time-dependent sequence-order, quantitative information, utility factor, and auxiliary dimension. Two algorithms respectively named MDUS EM and MDUS SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on six real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.
ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks
Publisher: ACM
Date: 14-08-2021
Multi-level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks
Publisher: ACM
Date: 19-04-2021
Single-shot Embedding Dimension Search in Recommender System
Publisher: ACM
Date: 06-07-2022
MCP: A multi-component learning machine to predict protein secondary structure
Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.COMPBIOMED.2019.04.040
Abstract: The Gene or DNA sequence in every cell does not control genetic properties on its own Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. Prediction of the protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein structure are expensive and time-consuming. Nevertheless, the average accuracy of the suggested solutions has hardly reached beyond 80%. The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbalance, and the high dimensionality of the encoding schemes. Furthermore, we utilize a compound string dissimilarity measure to directly interpret protein sequence content and avoid information loss. In order to improve accuracy, we employ two different classifiers including support vector machine and fuzzy nearest neighbor and collectively aggregate the classification outcomes to infer the final protein structures. We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches. The experimental results demonstrate that given a set of input sequences, our multi-component framework can accurately predict the protein structure. Nevertheless, the effectiveness of our unified model can be further enhanced through framework configuration.
exRumourLens: Auditable Rumour Detection with Multi-View Explanations
Publisher: IEEE
Date: 05-2022
Streaming Session-based Recommendation
Publisher: ACM
Date: 25-07-2019
Trustworthy Recommendation and Search: Introduction to the Special Section - Part 2
Publisher: Association for Computing Machinery (ACM)
Date: 28-07-2023
DOI: 10.1145/3604776
User Account Linkage Across Multiple Platforms with Location Data
Publisher: Springer Science and Business Media LLC
Date: 07-2020
Predicting Users’ Purchasing Behaviors Using Their Browsing History
Publisher: Springer International Publishing
Date: 2015
Gallat: A Spatiotemporal Graph Attention Network for Passenger Demand Prediction
Publisher: IEEE
Date: 04-2021
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
Publisher: No publisher found
Date: 2018
User identity linkage across social networks via linked heterogeneous network embedding
Publisher: Springer Science and Business Media LLC
Date: 23-04-2019
Restricted Boltzmann Machine Based Active Learning for Sparse Recommendation
Publisher: No publisher found
Date: 2018
Keyword-aware continuous kNN query on road networks
Publisher: IEEE
Date: 05-2016
From anomaly detection to rumour detection using data streams of social platforms
Publisher: Association for Computing Machinery (ACM)
Date: 05-2019
Abstract: Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics it spreads quickly through a dynamically evolving network and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach. In addition, we propose incremental methods, which enable us to detect rumours using streaming data of social platforms. We illustrate the effectiveness and efficiency of our approach with a real-world dataset of 4M tweets with more than 1000 rumours.
Joint-teaching: Learning to Refine Knowledge for Resource-constrained Unsupervised Cross-modal Retrieval
Publisher: ACM
Date: 17-10-2021
Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion
Publisher: Springer Science and Business Media LLC
Date: 14-12-2016
A deep dive into user display names across social networks
Publisher: Elsevier BV
Date: 06-2018
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Discovering Subsequence Patterns for Next POI Recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.
Spatiotemporal Recommendation in Geo-Social Networks
Publisher: Springer New York
Date: 2018
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
Publisher: ACM
Date: 25-07-2020
Online User Representation Learning Across Heterogeneous Social Networks
Publisher: ACM
Date: 18-07-2019
Understanding the User Display Names across Social Networks
Publisher: ACM Press
Date: 2017
Decoupling Representation Learning and Classification for GNN-based Anomaly Detection
Publisher: ACM
Date: 11-07-2021
StockAssIstant: A Stock AI Assistant for Reliability Modeling of Stock Comments
Publisher: ACM
Date: 19-07-2018
Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Semantic trajectory representation and retrieval via hierarchical embedding
Publisher: Elsevier BV
Date: 10-2020
Group-Based Recurrent Neural Networks for POI Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 29-02-2020
DOI: 10.1145/3343037
Abstract: With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network (GTSAR-RNN). We ide the users into different groups and then train an in idual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users’ sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.
Self-Supervised Learning for Recommender Systems: A Survey
Publisher: arXiv
Date: 2022
Graph Embedding for Recommendation against Attribute Inference Attacks
Publisher: ACM
Date: 19-04-2021
Group Recommender Model Based on Preference Interaction
Publisher: Springer International Publishing
Date: 2017
Adapting to User Interest Drift for POI Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion
Publisher: IEEE
Date: 04-2015
Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation
Publisher: ACM
Date: 13-10-2015
Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation
Publisher: ACM
Date: 17-10-2015
Argument discovery via crowdsourcing
Publisher: Springer Science and Business Media LLC
Date: 07-04-2017
A Time and Sentiment Unification Model for Personalized Recommendation
Publisher: Springer International Publishing
Date: 2017
A Decentralized Collaborative Learning Framework Across Heterogeneous Devices for Personalized Predictive Analytics
Publisher: arXiv
Date: 2022
Enhanced factorization machine via neural pairwise ranking and attention networks
Publisher: Elsevier BV
Date: 12-2020
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Publisher: No publisher found
Date: 2020
What-If Analysis with Conflicting Goals: Recommending Data Ranges for Exploration
Publisher: IEEE
Date: 04-2018
AutoML for Deep Recommender Systems: A Survey
Publisher: arXiv
Date: 2022
Scalable Robust Graph Embedding with Spark
Publisher: Association for Computing Machinery (ACM)
Date: 12-2021
Abstract: Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embedding technique. Our method splits a graph into subgraphs to learn their embeddings in isolation and subsequently reconciles the embedding spaces derived for the subgraphs. We realize this idea through a novel distributed graph decomposition algorithm. In addition, we show how to implement our framework in Spark to enable efficient learning of effective embeddings. Experimental results illustrate that our approach scales well, while largely maintaining the embedding quality.
TSAUB: A Temporal-Sentiment-Aware User Behavior Model for Personalized Recommendation
Publisher: No publisher found
Date: 2018
Exploiting detected visual objects for frame-level video filtering
Publisher: Springer Science and Business Media LLC
Date: 27-10-2017
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs
Publisher: arXiv
Date: 2022
Subgraph Convolutional Network for Recommendation
Publisher: IEEE
Date: 07-11-2021
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs
Publisher: Association for Computing Machinery (ACM)
Date: 29-11-2021
DOI: 10.1145/3446344
Abstract: In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat ( G raph prediction with all at tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Publisher: ACM
Date: 04-08-2017
Introduction to the Special Issue on Intelligent Trajectory Analytics: Part I
Publisher: Association for Computing Machinery (ACM)
Date: 23-12-2022
DOI: 10.1145/3495230
Efficient User Guidance for Validating Participatory Sensing Data
Publisher: Association for Computing Machinery (ACM)
Date: 17-07-2019
DOI: 10.1145/3326164
Abstract: Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely u G /u eneralised u A /u uto u R /u egressive u C /u onditional u H /u eteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.
Extracting Representative User Subset of Social Networks Towards User Characteristics and Topological Features
Publisher: Springer International Publishing
Date: 2018
Introduction to the Special Issue on Intelligent Trajectory Analytics: Part II
Publisher: Association for Computing Machinery (ACM)
Date: 22-04-2022
DOI: 10.1145/3510021
Group Recommender Model Based on Preference Interaction
Publisher: No publisher found
Date: 2017
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation
Publisher: Research Square Platform LLC
Date: 20-01-2023
DOI: 10.21203/RS.3.RS-2439540/V1
Abstract: As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized service providers must put unlearning functionality into their consideration. The most straightforward method to unlearn users’ contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralized learning scenarios. In this paper, we design a decentralized unlearning framework called HDUS, which uses distilled seed models to construct erasable ensembles in all clients. Moreover, the framework is compatible with heterogeneous on-device models, representing stronger scalability in real-world applications. Extensive experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
Publisher: Association for Computing Machinery (ACM)
Date: 07-02-2023
DOI: 10.1145/3560486
Abstract: Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model while maintaining data privacy. Typically, federated recommender systems (FRSs) do not take into account the lack of resources and data availability at the end-devices. In addition, they assume that the interaction data between users and items is i.i.d. and stationary across end-devices (i.e., users), and that all local recommender models can be directly averaged without considering the user’s behavioral ersity. However, in real scenarios, recommendations have to be made on end-devices with sparse interaction data and limited resources. Furthermore, users’ preferences are heterogeneous and they frequently visit new items. This makes their personal preferences highly skewed, and the straightforwardly aggregated model is thus ill-posed for such non-i.i.d. data. In this article, we propose Resource Efficient Federated Recommender System (ReFRS) to enable decentralized recommendation with dynamic and ersified user preferences. On the device side, ReFRS consists of a lightweight self-supervised local model built upon the variational autoencoder for learning a user’s temporal preference from a sequence of interacted items. On the server side, ReFRS utilizes a scalable semantic s ler to adaptively perform model aggregation within each identified cluster of similar users. The clustering module operates in an asynchronous and dynamic manner to support efficient global model update and cope with shifting user interests. As a result, ReFRS achieves superior performance in terms of both accuracy and scalability, as demonstrated by comparative experiments on real datasets.
Semi-supervised clustering with deep metric learning and graph embedding
Publisher: Springer Science and Business Media LLC
Date: 24-08-2019
Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach
Publisher: Association for Computing Machinery (ACM)
Date: 27-09-2021
DOI: 10.1145/3460198
Abstract: Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and erse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
Using Detected Visual Objects to Index Video Database
Publisher: Springer International Publishing
Date: 2016
Exploiting Positional Information for Session-Based Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 16-11-2021
DOI: 10.1145/3473339
Abstract: For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness . Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.
Efficient Bi-Level Optimization for Recommendation Denoising
Publisher: ACM
Date: 04-08-2023
Deep Pairwise Hashing for Cold-Start Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
Publisher: Springer International Publishing
Date: 2018
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Publisher: ACM
Date: 30-04-2023
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
Publisher: ACM
Date: 26-10-2021
Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation
Publisher: No publisher found
Date: 2017
Entity Alignment for Knowledge Graphs With Multi-Order Convolutional Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Finding a Wise Group of Experts in Social Networks
Publisher: No publisher found
Date: 2011
Mining Subgraphs from Propagation Networks through Temporal Dynamic Analysis
Publisher: IEEE
Date: 06-2018
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modeling
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Network Alignment with Holistic Embeddings (Extended Abstract)
Publisher: IEEE
Date: 05-2022
Exploiting Spatio-Temporal User Behaviors for User Linkage
Publisher: ACM
Date: 06-11-2017
Disease Prediction via Graph Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Proactive Privacy-preserving Learning for Cross-modal Retrieval
Publisher: Association for Computing Machinery (ACM)
Date: 25-01-2023
DOI: 10.1145/3545799
Abstract: Deep cross-modal retrieval techniques have recently achieved remarkable performance, which also poses severe threats to data privacy potentially. Nowadays, enormous user-generated contents that convey personal information are released and shared on the Internet. One may abuse a retrieval system to pinpoint sensitive information of a particular Internet user, causing privacy leakage. In this article, we propose a data-centric Proactive Privacy-preserving Cross-modal Learning algorithm that fulfills the protection purpose by employing a generator to transform original data into adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the inside adversarial data can confuse retrieval models under the attacker’s control to make erroneous predictions. We consider the protection under a realistic and challenging setting where the prior knowledge of malicious models is agnostic. To handle this, a surrogate retrieval model is instead introduced, acting as the target to fool. The whole network is trained under a game-theoretical framework, where the generator and the retrieval model persistently evolve to fight against each other. To facilitate the optimization, a Gradient Reversal Layer module is inserted between two models, enabling a one-step learning fashion. Extensive experiments on widely used realistic datasets prove the effectiveness of the proposed method.
Discovering Organized POI Groups in a City
Publisher: No publisher found
Date: 2015
A Multi-strategy-based Pre-training Method for Cold-start Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 25-01-2023
DOI: 10.1145/3544107
Abstract: The cold-start issue is a fundamental challenge in Recommender Systems. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and has shown great potential for cold-start recommendation. However, due to the over-smoothing problem, PT-GNN can only capture up to 3-order relation, which cannot provide much useful auxiliary information to depict the target cold-start user or item. Besides, the embedding reconstruction task only considers the intra-correlations within the subgraph of users and items, while ignoring the inter-correlations across different subgraphs. To solve the above challenges, we propose a multi-strategy-based pre-training method for cold-start recommendation (MPT), which extends PT-GNN from the perspective of model architecture and pretext tasks to improve the cold-start recommendation performance. 1 Specifically, in terms of the model architecture, in addition to the short-range dependencies of users and items captured by the GNN encoder, we introduce a Transformer encoder to capture long-range dependencies. In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add an embedding contrastive learning task to capture inter-correlations of users and items. We train the GNN and Transformer encoders on these pretext tasks under the meta-learning setting to simulate the real cold-start scenario, making the model able to be easily and rapidly adapted to new cold-start users and items. Experiments on three public recommendation datasets show the superiority of the proposed MPT model against the vanilla GNN models, the pre-training GNN model on user/item embedding inference, and the recommendation task.
Self-Supervised Hypergraph Representation Learning for Sociological Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Enhancing Collaborative Filtering with Generative Augmentation
Publisher: ACM
Date: 25-07-2019
Discrete ranking-based matrix factorization with self-paced learnings
Publisher: ACM
Date: 19-07-2018
TEAGS: time-aware text embedding approach to generate subgraphs
Publisher: Springer Science and Business Media LLC
Date: 03-06-2020
Origin-Destination Matrix Prediction via Graph Convolution
Publisher: ACM
Date: 25-07-2019
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
Publisher: ACM
Date: 20-08-2020
Semi-supervised Clustering with Deep Metric Learning
Publisher: Springer International Publishing
Date: 2019
Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Computing Crowd Consensus with Partial Agreement
Publisher: IEEE
Date: 04-2018
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Publisher: ACM
Date: 19-04-2021
Geographical Constraint and Temporal Similarity Modeling for Point-of-Interest Recommendation
Publisher: Springer International Publishing
Date: 2015
Finding a Wise Group of Experts in Social Networks
Publisher: Springer Berlin Heidelberg
Date: 2011
Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks
Publisher: arXiv
Date: 2022
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Publisher: arXiv
Date: 2022
Neural Pairwise Ranking Factorization Machine for Item Recommendation
Publisher: Springer International Publishing
Date: 2020
Influenced Nodes Discovery in Temporal Contact Network
Publisher: No publisher found
Date: 2017
LC-RNN: A Deep Learning Model for Traffic Speed Prediction
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms six well-known existing methods.
Decentralized Embedding Framework for Large-Scale Networks
Publisher: Springer International Publishing
Date: 2020
Recommendation in Context-Rich Environment: An Information Network Analysis Approach
Publisher: ACM Press
Date: 2017
EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviors
Publisher: Springer International Publishing
Date: 2020
Personalized Video Recommendation Using Rich Contents from Videos
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
Publisher: Association for Computing Machinery (ACM)
Date: 23-05-2020
DOI: 10.1145/3382764
Abstract: Different from the traditional recommender system, the session-based recommender system introduces the concept of the session , i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within in idual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel F ull G raph N eural N etwork (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the in idual session’s representation learning, we further construct a B roadly C onnected S ession (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BCS graph. Extensive experiments have been conducted on two e-commerce benchmark datasets, i.e., Yoochoose and Diginetica , and the experimental results demonstrate the superiority of our proposal through comparisons with state-of-the-art session-based recommender models.
A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network
Publisher: ACM
Date: 26-10-2021
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Start Date: 03-2019
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Amount: $350,000.00
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View Funded ActivityDiscovery Early Career Researcher Award - Grant ID: DE160100308
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Amount: $300,000.00
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View Funded ActivityDiscovery Projects - Grant ID: DP170103954
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