ORCID Profile
0000-0002-6468-5729
Current Organisations
University of Technology Sydney
,
Nanyang Technological University
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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)
Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) | Computer Software | Data mining and knowledge discovery | Information Systems Development Methodologies | Data engineering and data science | Information Systems Management | Computer Hardware not elsewhere classified | Software Engineering | Computer Hardware | Mechanical Engineering | Deep learning | Acoustics and Noise Control (excl. Architectural Acoustics) | Behavioural Ecology | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Application Tools and System Utilities | Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge in the Information and Computing Sciences | Property, Business Support Services and Trade not elsewhere classified | Control of Pests, Diseases and Exotic Species in Urban and Industrial Environments | Information and Communication Services not elsewhere classified | Expanding Knowledge in Engineering | Expanding Knowledge in the Biological Sciences |
Related Links
Publications
DECK: Detecting Events from Web Click-Through Data
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/ICDM.2008.78
Mining Positive and Negative Association Rules from XML Query Patterns for Caching
Publisher: No publisher found
Date: 2005
DOI: 10.1007/11408079\_67
Mining Probabilistic Representative Frequent Patterns From Uncertain Data
Publisher: Society for Industrial and Applied Mathematics
Date: 02-05-2013
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.
Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Binarized attributed network embedding
Publisher: IEEE
Date: 11-2018
Progressive Stochastic Learning for Noisy Labels
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Mining Co-locations from Continuously Distributed Uncertain Spatial Data
Publisher: Springer International Publishing
Date: 2016
Defragging Subgraph Features for Graph Classification
Publisher: ACM
Date: 17-10-2015
COWES: Web user clustering based on evolutionary web sessions
Publisher: Elsevier BV
Date: 10-2009
Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
RCP Mining: Towards the Summarization of Spatial Co-location Patterns
Publisher: No publisher found
Date: 2015
Memetic Extreme Learning Machine
Publisher: Elsevier BV
Date: 10-2016
Online diffusion source detection in social networks
Publisher: IEEE
Date: 07-2015
ASTM: An Attentional Segmentation Based Topic Model for Short Texts
Publisher: IEEE
Date: 11-2018
Improved Consistent Weighted Sampling Revisited
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Noisy but non-malicious user detection in social recommender systems
Publisher: Springer Science and Business Media LLC
Date: 14-04-2012
Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 17-07-2019
DOI: 10.1609/AAAI.V33I01.33014408
Abstract: In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For ex le, in fraud detection, the number of positive ex les are much less than negative ex les because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.
Towards the Learning of Weighted Multi-label Associative Classifiers
Publisher: IEEE
Date: 07-2018
Consistent Weighted Sampling Made More Practical
Publisher: International World Wide Web Conferences Steering Committee
Date: 03-04-2017
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.
Privacy-Preserving Stochastic Gradual Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Summarizing probabilistic frequent patterns
Publisher: ACM
Date: 11-08-2013
Mining Maximal Frequently Changing Subtree Patterns from XML Documents
Publisher: No publisher found
Date: 2004
Mining Dependent Frequent Serial Episodes from Uncertain Sequence Data
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/ICDM.2013.35
Cross-View Feature Hashing for Image Retrieval
Publisher: No publisher found
Date: 2016
Advanced Data Mining and Applications - 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
Publisher: Springer Berlin Heidelberg
Date: 2011
Discrete Multi-Graph Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
SocialAnalysis: A Real-Time Query and Mining System from Social Media Data Streams
Publisher: No publisher found
Date: 2015
K-Ary Tree Hashing for Fast Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Semi-supervised Variable Weighting for Clustering
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2011
Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Mining Positive and Negative Association Rules from XML Query Patterns for Caching
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11408079_67
Canonical Consistent Weighted Sampling for Real-Value Weighted Min-Hash
Publisher: IEEE
Date: 12-2016
RCP Mining: Towards the Summarization of Spatial Co-location Patterns
Publisher: Springer International Publishing
Date: 2015
FRACTURE mining: Mining frequently and concurrently mutating structures from historical XML documents
Publisher: Elsevier BV
Date: 11-2006
Towards large-scale social networks with online diffusion provenance detection
Publisher: Elsevier BV
Date: 02-2017
Mining Association Rules from Structural Deltas of Historical XML Documents
Publisher: Springer Berlin Heidelberg
Date: 2004
Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification
Publisher: Association for Computing Machinery (ACM)
Date: 27-01-2020
DOI: 10.1145/3372121
Abstract: The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled s le available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less erse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the ersity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and erse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Encrypted data indexing for the secure outsourcing of spectral clustering
Publisher: Springer Science and Business Media LLC
Date: 07-09-2019
In the Search of NECTARs from Evolutionary Trees
Publisher: Springer Berlin Heidelberg
Date: 2009
Build Emotion Lexicon from the Mood of Crowd via Topic-Assisted Joint Non-negative Matrix Factorization
Publisher: ACM
Date: 07-07-2016
Incremental Subgraph Feature Selection for Graph Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Mining Co-locations from Continuously Distributed Uncertain Spatial Data
Publisher: No publisher found
Date: 2016
Online compressed robust PCA
Publisher: IEEE
Date: 05-2017
Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
The Art of Tagging: Measuring the Quality of Tags
Publisher: Springer Berlin Heidelberg
Date: 2009
Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
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.
Deep Reinforcement Learning with Transformers for Text Adventure Games
Publisher: IEEE
Date: 08-2020
COWES: Clustering Web Users Based on Historical Web Sessions
Publisher: No publisher found
Date: 2006
DOI: 10.1007/11733836\_38
Mining Temporal Indirect Associations
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_49
Mining Temporal Indirect Associations
Publisher: No publisher found
Date: 2006
DOI: 10.1007/11731139\_49
Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous Graph
Publisher: ACM Press
Date: 2015
Introduction to Special section on Large-scale Data Mining
Publisher: Elsevier BV
Date: 09-2013
User identity linkage across social networks via linked heterogeneous network embedding
Publisher: Springer Science and Business Media LLC
Date: 23-04-2019
XML structural delta mining: Issues and challenges
Publisher: Elsevier BV
Date: 12-2006
Efficient Attributed Network Embedding via Recursive Randomized Hashing
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., the predefined highest-order neighboring nodes) to top (i.e., the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.
Advanced Data Mining and Applications - 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I
Publisher: Springer Berlin Heidelberg
Date: 2011
The Art of Tagging: Measuring the Quality of Tags
Publisher: No publisher found
Date: 2008
Using subspace analysis for event detection from web click-through data
Publisher: ACM
Date: 21-04-2008
Improving music genre classification using collaborative tagging data
Publisher: ACM
Date: 09-02-2009
SocialAnalysis: A Real-Time Query and Mining System from Social Media Data Streams
Publisher: Springer International Publishing
Date: 2015
Low-Bit Quantization for Attributed Network Representation Learning
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.
Mirror site maintenance based on evolution associations of web directories
Publisher: ACM
Date: 08-05-2007
COWES: Clustering Web Users Based on Historical Web Sessions
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11733836_38
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent
Publisher: No publisher found
Date: 2016
Mining Maximal Frequently Changing Subtree Patterns from XML Documents
Publisher: Springer Berlin Heidelberg
Date: 2004
Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
A temporal context-aware model for user behavior modeling in social media systems
Publisher: ACM
Date: 18-06-2014
Mining Association Rules from Structural Deltas of Historical XML Documents
Publisher: No publisher found
Date: 2004
Protecting Location Privacy in Spatial Crowdsourcing using Encrypted Data
Publisher: No publisher found
Date: 2017
VRules: an effective association-based classifier for videos
Publisher: ACM
Date: 13-11-2004
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.
Cross-View Feature Hashing for Image Retrieval
Publisher: Springer International Publishing
Date: 2016
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent
Publisher: Springer International Publishing
Date: 2016
LCARS: a location-content-aware recommender system
Publisher: ACM
Date: 11-08-2013
Personalized Video Recommendation Using Rich Contents from Videos
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Mining frequent serial episodes over uncertain sequence data
Publisher: ACM
Date: 18-03-2013
Dynamic Slimmable Denoising Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
In the Search of NECTARs from Evolutionary Trees
Publisher: No publisher found
Date: 2009
Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
NEAR-Miner: Mining Evolution Associations of Web Site Directories for Efficient Maintenance of Web Archives
Publisher: Association for Computing Machinery (ACM)
Date: 08-2009
Abstract: Web archives preserve the history of autonomous Web sites and are potential gold mines for all kinds of media and business analysts. The most common Web archiving technique uses crawlers to automate the process of collecting Web pages. However, (re)downloading entire collection of pages periodically from a large Web site is unfeasible. In this paper, we take a step towards addressing this problem. We devise a data mining-driven policy for selectively (re)downloading Web pages that are located in hierarchical directory structures which are believed to have changed significantly (e.g., a substantial percentage of pages are inserted to/removed from the directory). Consequently, there is no need to download and maintain pages that have not changed since the last crawl as they can be easily retrieved from the archive. In our approach, we propose an off-line data mining algorithm called near- Miner that analyzes the evolution history of Web directory structures of the original Web site stored in the archive and mines negatively correlated association rules (near) between ancestor-descendant Web directories. These rules indicate the evolution correlations between Web directories. Using the discovered rules, we propose an efficient Web archive maintenance algorithm called warm that optimally skips the subdirectories (during the next crawl) which are negatively correlated with it in undergoing significant changes. Our experimental results with real data show that our approach improves the efficiency of the archive maintenance process significantly while sacrificing slightly in keeping the "freshness" of the archives. Furthermore, our experiments demonstrate that it is not necessary to discover nears frequently as the mining rules can be utilized effectively for archive maintenance over multiple versions.
SPORE: A sequential personalized spatial item recommender system
Publisher: IEEE
Date: 05-2016
Related Organisations
Related Funding Activities
ARC Future Fellowships - Grant ID: FT230100121
Start Date: 06-2024
End Date: 06-2028
Amount: $1,140,382.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP140100569
Start Date: 09-2015
End Date: 10-2015
Amount: $510,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP120100566
Start Date: 2012
End Date: 05-2015
Amount: $380,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140100545
Start Date: 2014
End Date: 09-2017
Amount: $390,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP210101348
Start Date: 2021
End Date: 12-2024
Amount: $300,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE140100061
Start Date: 2014
End Date: 07-2015
Amount: $1,000,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP210101347
Start Date: 2021
End Date: 06-2024
Amount: $368,810.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP200301196
Start Date: 07-2022
End Date: 06-2026
Amount: $580,165.00
Funder: Australian Research Council
View Funded Activity