Search Results for author: Junliang Yu

Found 30 papers, 18 papers with code

Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

1 code implementation6 Jul 2024 Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Chen Chen

Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles.

Contrastive Learning Graph Learning +1

Poisoning Attacks and Defenses in Recommender Systems: A Survey

1 code implementation3 Jun 2024 Zongwei Wang, Junliang Yu, Min Gao, Wei Yuan, Guanhua Ye, Shazia Sadiq, Hongzhi Yin

Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks.

Recommendation Systems

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

no code implementations22 May 2024 Xinyi Gao, Tong Chen, Wentao Zhang, Junliang Yu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements.

Graph Neural Network

Graph Condensation: A Survey

no code implementations22 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).

Fairness Graph Generation

Poisoning Attacks against Recommender Systems: A Survey

1 code implementation3 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).

Recommendation Systems

Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks

1 code implementation30 Nov 2023 Zongwei Wang, Junliang Yu, Min Gao, Hongzhi Yin, Bin Cui, Shazia Sadiq

Our analysis indicates that this vulnerability is attributed to the uniform spread of representations caused by the InfoNCE loss.

Contrastive Learning Recommendation Systems

Motif-Based Prompt Learning for Universal Cross-Domain Recommendation

no code implementations20 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.

General Knowledge Multi-Task Learning

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation

no code implementations17 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.

Graph Neural Network

Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation

1 code implementation24 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.

Session-Based Recommendations

Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks

no code implementations27 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).

Representation Learning

Efficient Bi-Level Optimization for Recommendation Denoising

2 code implementations19 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.

Data Augmentation Denoising +1

Efficient On-Device Session-Based Recommendation

1 code implementation27 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.

Knowledge Distillation Model Compression +1

XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

1 code implementation6 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.

Contrastive Learning

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

1 code implementation23 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).

Knowledge Distillation Recommendation Systems +1

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation29 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.

Recommendation Systems Self-Supervised Learning

Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

no code implementations8 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.

Auxiliary Learning Self-Supervised Learning

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

no code implementations19 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.

Marketing reinforcement-learning +1

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

1 code implementation16 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.

Contrastive Learning Recommendation Systems

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

1 code implementation9 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.

Self-Supervised Graph Co-Training for Session-based Recommendation

2 code implementations24 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.

Contrastive Learning Data Augmentation +2

Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack

no code implementations22 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.

Generative Adversarial Network Recommendation Systems

Socially-Aware Self-Supervised Tri-Training for Recommendation

1 code implementation7 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.

Contrastive Learning Recommendation Systems +2

Fast-adapting and Privacy-preserving Federated Recommender System

no code implementations2 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.

Federated Learning Meta-Learning +2

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

4 code implementations16 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.

Recommendation Systems Self-Supervised Learning

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2 code implementations12 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.

Self-Supervised Learning Session-Based Recommendations

Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling

1 code implementation10 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.

Explainable Recommendation Recommendation Systems

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations5 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.

Recommendation Systems

Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

no code implementations5 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.

Data Augmentation Recommendation Systems

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations8 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.

Recommendation Systems

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