1 code implementation • 26 Jan 2024 • Lei Guo, Ziang Lu, Junliang Yu, Nguyen Quoc Viet Hung, Hongzhi Yin
For Limitation 2, we model items in a universal feature space by their description texts.
no code implementations • 22 Jan 2024 • Xinyi Gao, Junliang Yu, Wei Jiang, Tong Chen, Wentao Zhang, Hongzhi Yin
The burgeoning volume of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs).
1 code implementation • 3 Jan 2024 • Zongwei Wang, Min Gao, Junliang Yu, Hao Ma, Hongzhi Yin, Shazia Sadiq
This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR).
no code implementations • 30 Nov 2023 • Zongwei Wang, Junliang Yu, Min Gao, Hongzhi Yin, Bin Cui, Shazia Sadiq
Contrastive learning (CL) has recently gained significant popularity in the field of recommendation.
no code implementations • 20 Oct 2023 • Bowen Hao, Chaoqun Yang, Lei Guo, Junliang Yu, Hongzhi Yin
By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.
no code implementations • 17 Oct 2023 • Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin
To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.
1 code implementation • 24 Aug 2023 • Xin Xia, Junliang Yu, Guandong Xu, Hongzhi Yin
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.
no code implementations • 27 Feb 2023 • Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Hung Quoc Viet Nguyen, Hongzhi Yin
To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).
2 code implementations • 19 Oct 2022 • Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, Hongzhi Yin
To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.
1 code implementation • 27 Sep 2022 • Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.
1 code implementation • 6 Sep 2022 • Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, Hongzhi Yin
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.
1 code implementation • 23 Apr 2022 • Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Nguyen Quoc Viet Hung
Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
1 code implementation • 29 Mar 2022 • Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang
In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data.
no code implementations • 8 Mar 2022 • Yinghui Tao, Min Gao, Junliang Yu, Zongwei Wang, Qingyu Xiong, Xu Wang
To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.
no code implementations • 19 Feb 2022 • Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin
By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.
1 code implementation • 16 Dec 2021 • Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.
1 code implementation • 9 Sep 2021 • Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin
Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.
2 code implementations • 24 Aug 2021 • Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, Lizhen Cui
In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.
no code implementations • 22 Jul 2021 • Fan Wu, Min Gao, Junliang Yu, Zongwei Wang, Kecheng Liu, Xu Wange
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
1 code implementation • 7 Jun 2021 • Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
no code implementations • 2 Apr 2021 • Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.
4 code implementations • 16 Jan 2021 • Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang
In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.
2 code implementations • 12 Dec 2020 • Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
1 code implementation • 10 Aug 2020 • Junwei Zhang, Min Gao, Junliang Yu, Linda Yang, Zongwei Wang, Qingyu Xiong
Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited.
no code implementations • 5 Apr 2020 • Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.
no code implementations • 5 Mar 2020 • Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, Qingyu Xiong
In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.
no code implementations • 8 Sep 2019 • Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.