ORCID Profile
0000-0002-9738-4949
Current Organisation
University of Queensland
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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.
Information Systems | Pattern Recognition and Data Mining | Database Management | Global Information Systems | Crop and pasture production | Family and Household Studies | Animal reproduction and breeding | Horticultural crop improvement (incl. selection and breeding) | Infrastructure Engineering and Asset Management | Crop and pasture improvement (incl. selection and breeding) | Environmental Engineering Modelling | Social Change | Environmental Engineering | Data mining and knowledge discovery | Data and information privacy | Information Systems Management | Distributed systems and algorithms | Database systems | Cyberphysical systems and internet of things | Demography | Social Policy | Manufacturing robotics | Data management and data science | Distributed computing and systems software
Electronic Information Storage and Retrieval Services | Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge through Studies of Human Society | Water Services and Utilities | Urban and Industrial Water Management | Application Tools and System Utilities | Families and Family Services | Social Class and Inequalities |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 24-02-2011
Publisher: IEEE
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 24-06-2021
Publisher: Elsevier BV
Date: 04-2023
Publisher: IEEE
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: ACM
Date: 10-10-2022
Publisher: IEEE
Date: 04-2017
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.
Publisher: IEEE
Date: 12-2021
Publisher: ACM
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Association for Computing Machinery (ACM)
Date: 07-06-2023
DOI: 10.1145/3587252
Abstract: Video captioning aims to automatically generate natural language sentences describing the content of a video. Although encoder-decoder-based models have achieved promising progress, it is still very challenging to effectively model the linguistic behavior of humans in generating video captions. In this paper, we propose a novel video captioning model by learning from gLobal sEntence and looking AheaD, LEAD for short. Specifically, LEAD consists of two modules: a Vision Module (VM) and a Language Module (LM) . Thereinto, VM is a novel attention network, which can map visual features to high-level language space and model entire sentences explicitly. LM can not only effectively make use of the information of the previous sequence when generating the current word, but also have a look at the future word. Therefore, based on VM and LM, LEAD can obtain global sentence information and future word information to make video captioning more like a fill-in-the-blank task than a word-by-word sentence generation. In addition, we also propose an autonomous strategy and a multi-stage training scheme to optimize the model, which can mitigate the problem of information leakage. Extensive experiments show that LEAD outperforms some state-of-the-art methods on MSR-VTT, MSVD, and VATEX, demonstrating the effectiveness of the proposed approach in video captioning. In addition, we release the code of our proposed model to be publicly available. 1
Publisher: IEEE
Date: 06-2009
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 10-2017
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Image captioning aims to describe an image with a concise, accurate, and interesting sentence. To build such an automatic neural captioner, the traditional models align the generated words with a number of human-annotated sentences to mimic human-like captions. However, the crowd-sourced annotations inevitably come with data quality issues such as grammatical errors, wrong identification of visual objects and sub-optimal sentence focus. During the model training, existing methods treat all the annotations equally regardless of the data quality. In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority. Therefore, the proposed consensus-oriented method can accelerate the training process and achieve superior performance with only supervised objective without time-consuming reinforcement learning. The novel consensus loss can be implemented into most of the existing state-of-the-art methods, boosting the BLEU-4 performance by maximum relative 12.47% comparing to the conventional cross-entropy loss. Extensive experiments are conducted on MS-COCO Image Captioning dataset demonstrating the proposed human consensus-oriented training method can significantly improve the training efficiency and model effectiveness.
Publisher: IEEE
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 05-2013
Publisher: Springer Science and Business Media LLC
Date: 11-10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 03-2011
Publisher: IEEE
Date: 03-2009
DOI: 10.1109/ICDE.2009.17
Publisher: Springer Science and Business Media LLC
Date: 23-08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: ACM
Date: 23-10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: ACM
Date: 11-02-2022
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2008
Publisher: ACM
Date: 02-02-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: ACM
Date: 12-10-2020
Publisher: ACM
Date: 28-11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: Elsevier BV
Date: 10-2018
Publisher: ACM
Date: 13-12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 27-06-2018
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 18-06-2014
Publisher: ACM
Date: 28-11-2011
Publisher: ACM
Date: 30-04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. With each task forming as a graph, the intra- and inter-task correlations can be well preserved via message-passing and history transition. To remedy topological uncertainty from graph initialization, we utilize Bayes by Backprop strategy that approximates the posterior distribution of task-specific parameters with amortized inference networks, which are seamlessly integrated into the end-to-end edge learning. Extensive experiments conducted on the miniImageNet and tieredImageNet datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the miniImageNet 5-way 1-shot classification task.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Association for Computing Machinery (ACM)
Date: 30-05-2023
DOI: 10.1145/3588572
Abstract: Video captioning aims to automatically describe a video clip with informative sentences. At present, deep learning-based models have become the mainstream for this task and achieved competitive results on public datasets. Usually, these methods leverage different types of features to generate sentences, e.g., semantic information, 2D or 3D features. However, some methods only treat semantic information as a complement of visual representations and cannot fully exploit it some of them ignore the relationship between different types of features. In addition, most of them select multiple frames of a video with an equally spaced s ling scheme, resulting in much redundant information. To address these issues, we present a novel video-captioning framework, Semantic Enhanced video captioning with Multi-feature Fusion, SEMF for short. It optimizes the use of different types of features from three aspects. First, a semantic encoder is designed to enhance meaningful semantic features through a semantic dictionary to boost performance. Second, a discrete selection module pays attention to important features and obtains different contexts at different steps to reduce feature redundancy. Finally, a multi-feature fusion module uses a novel relation-aware attention mechanism to separate the common and complementary components of different features to provide more effective visual features for the next step. Moreover, the entire framework can be trained in an end-to-end manner. Extensive experiments are conducted on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets. The results demonstrate that SEMF is able to achieve state-of-the-art results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 07-2017
Publisher: ACM
Date: 10-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: ACM
Date: 12-06-2011
Publisher: ACM
Date: 11-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: ACM
Date: 05-06-2018
Publisher: ACM
Date: 15-10-2019
Publisher: ACM
Date: 15-10-2019
Publisher: ACM
Date: 05-06-2018
Publisher: ACM
Date: 11-07-2021
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Publisher: Springer International Publishing
Date: 2021
Publisher: PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.
Date: 2005
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: ACM
Date: 07-08-2017
Publisher: Springer Science and Business Media LLC
Date: 02-06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Springer International Publishing
Date: 2019
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.
Publisher: Springer Science and Business Media LLC
Date: 02-10-2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 22-12-2010
Publisher: Elsevier BV
Date: 02-2015
Publisher: ACM
Date: 03-11-2019
Publisher: IEEE
Date: 04-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 07-07-2020
Publisher: ACM
Date: 06-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 20-04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Publisher: IEEE
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 27-02-2021
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 21-10-2013
Publisher: Elsevier BV
Date: 03-2018
Publisher: Springer Science and Business Media LLC
Date: 31-03-2023
Publisher: ACM
Date: 17-10-2021
Publisher: ACM
Date: 12-10-2020
Publisher: ACM
Date: 14-08-2021
Publisher: Springer Science and Business Media LLC
Date: 22-03-2019
Publisher: Springer Science and Business Media LLC
Date: 10-10-2018
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 15-10-2019
Publisher: ACM
Date: 13-10-2015
Publisher: IEEE
Date: 2003
Publisher: Association for Computing Machinery (ACM)
Date: 05-2014
Abstract: Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, state-of-the-art LSH approaches suffer from a drawback: accesses to candidate objects require a large number of random I/O operations. In order to guarantee the quality of returned results, sufficient objects should be verified, which would consume enormous I/O cost. To address this issue, we propose a novel method, called SortingKeys-LSH (SK-LSH), which reduces the number of page accesses through locally arranging candidate objects. We firstly define a new measure to evaluate the distance between the compound hash keys of two points. A linear order relationship on the set of compound hash keys is then created, and the corresponding data points can be sorted accordingly. Hence, data points that are close to each other according to the distance measure can be stored locally in an index file. During the ANN search, only a limited number of disk pages among few index files are necessary to be accessed for sufficient candidate generation and verification, which not only significantly reduces the response time but also improves the accuracy of the returned results. Our exhaustive empirical study over several real-world data sets demonstrates the superior efficiency and accuracy of SK-LSH for the ANN search, compared with state-of-the-art methods, including LSB, C2LSH and CK-Means.
Publisher: ACM
Date: 15-10-2018
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 18-05-2021
Abstract: Deep Neural Networks (DNNs) have recently achieved remarkable performance in image retrieval, yet posing great threats to data privacy. On the one hand, one may misuse a deployed DNNs based system to look up data without consent. On the other hand, organizations or in iduals would legally or illegally collect data to train high-performance models outside the scope of legitimate purposes. Unfortunately, less effort has been made to safeguard data privacy against malicious uses of DNNs. In this paper, we propose a data-centric Proactive Privacy-preserving Learning (PPL) algorithm for hashing based retrieval, which achieves the protection purpose by employing a generator to transfer the original data into the adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the adversarial data can confuse menacing retrieval models to make erroneous predictions. Given that the prior knowledge of malicious models is not available, a surrogate retrieval model is instead introduced acting as a fooling target. The framework is trained by a two-player game conducted between the generator and the surrogate model. More specifically, the generator is updated to enlarge the gap between the adversarial data and the original data, aiming to lower the search accuracy of the surrogate model. On the contrary, the surrogate model is trained with the opposing objective that is to maintain the search performance. As a result, an effective and robust adversarial generator is encouraged. Furthermore, to facilitate an effective optimization, a Gradient Reversal Layer (GRL) module is inserted to connect two models, enabling the two-player game in a one-step learning. Extensive experiments on three widely-used realistic datasets prove the effectiveness of the proposed method.
Publisher: Association for Computing Machinery (ACM)
Date: 05-2009
Abstract: Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant clips. Different from traditional long videos, a video clip is a short video often expressing a moment of significance. Due to the high complexity of video data, efficient video clip search from large databases turns out to be very challenging. We propose a novel video clip representation model called the Bounded Coordinate System (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip. It summarizes a video clip by a coordinate system, where each of its coordinate axes is identified by principal component analysis (PCA) and bounded by the range of data projections along the axis. The similarity measure of BCS considers the operations of translation, rotation, and scaling for coordinate system matching. Particularly, rotation and scaling reflect the difference of content tendencies. Compared with the quadratic time complexity of existing methods, the time complexity of measuring BCS similarity is linear. The compact video representation together with its linear similarity measure makes real-time search from video clip collections feasible. To further improve the retrieval efficiency for large video databases, a two-dimensional transformation method called Bidistance Transformation (BDT) is introduced to utilize a pair of optimal reference points with respect to bidirectional axes in BCS. Our extensive performance study on a large database of more than 30,000 video clips demonstrates that BCS achieves very high search accuracy according to human judgment. This indicates that content tendencies are important in determining the meanings of video clips and confirms that BCS can capture the inherent moment of video clip to some extent that better resembles human perception. In addition, BDT outperforms existing indexing methods greatly. Integration of the BCS model and BDT indexing can achieve real-time search from large video clip databases.
Publisher: ACM
Date: 19-07-2018
Publisher: IEEE
Date: 07-2019
Publisher: Springer Science and Business Media LLC
Date: 28-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: ACM
Date: 12-10-2020
Publisher: Association for Computing Machinery (ACM)
Date: 12-2011
Abstract: The unprecedented and ever-growing number of Web videos nowadays leads to the massive existence of near-duplicate videos. Very often, some near-duplicate videos exhibit great content changes, while the user perceives little information change, for ex le, color features change significantly when transforming a color video with a blue filter. These feature changes contribute to low-level video similarity computations, making conventional similarity-based near-duplicate video retrieval techniques incapable of accurately capturing the implicit relationship between two near-duplicate videos with fairly large content modifications. In this paper, we introduce a new dimension for near-duplicate video retrieval. Different from existing near-duplicate video retrieval approaches which are based on video-content similarity, we explore the correlation between two videos. The intuition is that near-duplicate videos should preserve strong information correlation in spite of intensive content changes. More effective retrieval with stronger tolerance is achieved by replacing video-content similarity measures with information correlation analysis. Theoretical justification and experimental results prove the effectiveness of correlation-based near-duplicate retrieval.
Publisher: ACM
Date: 29-10-2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Association for Computing Machinery (ACM)
Date: 08-2013
Abstract: The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted a lot of attention. As discovered in recent works, latest improvements and progress in near-duplicate video retrieval, as well as related topics including low-level feature extraction, signature generation, and high-dimensional indexing, are employed to assist the process. As we survey the works in near-duplicate video retrieval, we comparatively investigate existing variants of the definition of near-duplicate video, describe a generic framework, summarize state-of-the-art practices, and explore the emerging trends in this research topic.
Publisher: ACM
Date: 26-10-2023
Publisher: Elsevier BV
Date: 2013
Publisher: ACM
Date: 29-10-2012
Publisher: ACM
Date: 23-07-2007
Publisher: ACM
Date: 19-10-2017
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: Springer Science and Business Media LLC
Date: 16-03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: ACM
Date: 12-10-2020
Publisher: ACM
Date: 17-10-2021
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: IEEE
Date: 04-2008
DOI: 10.1109/INGS.2008.16
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Elsevier BV
Date: 11-2016
Publisher: ACM
Date: 25-07-2020
Publisher: Association for Computing Machinery (ACM)
Date: 11-2010
Abstract: Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the in idual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, erse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 18-07-2022
Publisher: Association for Computing Machinery (ACM)
Date: 03-2017
Abstract: Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.
Publisher: IEEE
Date: 04-2013
Publisher: ACM
Date: 19-04-2021
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 13-10-2015
Publisher: Association for Computing Machinery (ACM)
Date: 05-2013
Abstract: Hash-based methods achieve fast similarity search by representing high-dimensional data with compact binary codes. However, both generating binary codes and encoding unseen data effectively and efficiently remain very challenging tasks. In this article, we focus on these tasks to implement approximate similarity search by proposing a novel hash based method named sparse hashing (SH for short). To generate interpretable (or semantically meaningful) binary codes, the proposed SH first converts original data into low-dimensional data through a novel nonnegative sparse coding method. SH then converts the low-dimensional data into Hamming space (i.e., binary encoding low-dimensional data) by a new binarization rule. After this, training data are represented by generated binary codes. To efficiently and effectively encode unseen data, SH learns hash functions by taking a-priori knowledge into account, such as implicit group effect of the features in training data, and the correlations between original space and the learned Hamming space. SH is able to perform fast approximate similarity search by efficient bit XOR operations in the memory of a modern PC with short binary code representations. Experimental results show that the proposed SH significantly outperforms state-of-the-art techniques.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 03-11-2018
Publisher: ACM
Date: 10-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: ACM
Date: 26-10-2008
Publisher: Springer Science and Business Media LLC
Date: 13-05-2022
DOI: 10.1007/S11280-022-01056-9
Abstract: Sequential recommendation is a stream of studies on recommender systems, which focuses on predicting the next item a user interacts with by modeling the dynamic sequence of user-item interactions. Since being born to explore the dynamic tendency of variable-length temporal sequence, Recurrent Neural Networks (RNNs) have been paid much attention in this area. However, the inherent defects caused by the network structure of RNNs have limited their applications in sequential recommendation, which are mainly shown on two factors: RNNs tend to make point-wise predictions and ignore the collective dependencies because the temporal relationships between items change monotonically RNNs are likely to forget the essential information during processing long sequences. To solve these problems, researchers have done much work to enhance the memory mechanism of RNNs. However, although previous RNN-based methods have achieved promising performance by taking advantage of external knowledge with other advanced techniques, the improvement of the intrinsic property of existing RNNs has not been explored, which is still challenging. Therefore, in this work, we propose a novel architecture based on Long Short-Term Memories (LSTMs), a broadly-used variant of RNNs, specific for sequential recommendation, called Long Short-Term enhanced Memory (LSTeM) , which boosts the memory mechanism of original LSTMs in two ways. Firstly, we design a new structure of gates in LSTMs by introducing a “Q-K-V” triplet, a mechanism to accurately and properly model the correlation between the current item and the user’s historical behaviors at each time step. Secondly, we propose a “recover gate” to remedy the inadequacy of memory caused by the forgetting mechanism, which works with a dynamic global memory embedding. Extensive experiments have demonstrated that LSTeM achieves comparable performance to the state-of-the-art methods on the challenging datasets for sequential recommendation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 27-10-2017
Publisher: IEEE
Date: 11-2022
Publisher: ACM
Date: 07-08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: ACM
Date: 17-10-2021
Publisher: ACM
Date: 18-07-2019
Publisher: IEEE
Date: 04-2010
Publisher: Springer Science and Business Media LLC
Date: 10-06-2014
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11827405
Publisher: ACM
Date: 19-10-2017
Publisher: ACM
Date: 06-11-2017
Publisher: Springer International Publishing
Date: 2016
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.
Publisher: IEEE
Date: 10-2021
Publisher: ACM
Date: 17-10-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: ACM
Date: 29-09-2007
Publisher: IEEE
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: ACM
Date: 28-11-2011
Publisher: Elsevier BV
Date: 08-2013
Publisher: IEEE
Date: 2007
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.
Publisher: ACM
Date: 17-10-2021
Publisher: ACM
Date: 24-10-2016
Publisher: IEEE
Date: 07-2019
Publisher: ACM
Date: 17-10-2021
Publisher: ACM
Date: 17-10-2021
Publisher: ACM
Date: 25-07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Publisher: IEEE
Date: 04-2018
Publisher: ACM
Date: 22-06-2013
Publisher: IEEE
Date: 10-2021
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: ACM
Date: 19-10-2009
Publisher: Springer Berlin Heidelberg
Date: 2013
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.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 26-10-2023
Publisher: ACM
Date: 17-10-2021
Publisher: Springer International Publishing
Date: 2014
Start Date: 2014
End Date: 2016
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 2017
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2019
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2018
Funder: National Natural Science Foundation of China
View Funded ActivityStart Date: 2019
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 2011
End Date: 2013
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2014
End Date: 01-2019
Amount: $720,320.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 12-2027
Amount: $32,137,008.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2023
End Date: 12-2026
Amount: $485,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2011
End Date: 12-2013
Amount: $267,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 12-2020
Amount: $268,500.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 12-2017
Amount: $452,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2024
End Date: 06-2029
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2019
End Date: 12-2022
Amount: $262,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2023
End Date: 09-2028
Amount: $875,432.00
Funder: Australian Research Council
View Funded Activity