Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

By Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Li-zhen Cui, Xiangliang Zhang. AAAI Conference on Artificial Intelligence, 2020

632 citations recorded by the source corpus.

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a dual channel hypergraph convolutional network -- DHCN to improve SBR. Moreover, to enhance hypergraph modeling, we innovatively integrate self-supervised learning into the training of our network by maximizing mutual information between the session representations learned via the two channels in DHCN, serving as an auxiliary task to improve the recommendation task. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the ablation study validates the effectiveness and rationale of hypergraph modeling and self-supervised task. The implementation of our model is available via https://github.com/xiaxin1998/DHCN.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c3ef11a7a8a46f1f193250dd68838cb72b7de0bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Yin
2. Xiangnan He
3. Xiang Wang
4. Tong Chen
5. Junliang Yu
6. Xin Xia
7. Yong Li
8. Chen Gao

In Information Systems

1. Hongzhi Yin
2. Xiangnan He
3. Xiang Wang
4. Tong Chen
5. Junliang Yu
6. Xin Xia
7. Meng Wang
8. Yong Li

Citations in the corpus

• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022, methodology): “Inspired by the success of CL in other fields, the community also has started to integrate CL into recommendation [12], [13], [14], [15], [16], [17], [59].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “Besides, self-supervised learning [259], [260] is becoming emerged and showing promises in recommendation tasks [261], [262], [263], [264], [265].”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, background): “Since the success of contrastive learning in CV [2], contrastive learning has been widely applied on NLP [5], graph data mining [17, 34] and recommender systems [28, 37].”
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (2021, background): “DHCN [48] and MHCN [54] are graph-based recommenders, which conducts contrastive learning on the node-level representation.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “Sequential Recommendation Precision SR-GNN [174], FGNN [114], GCE-GNN [162], COTREC [180], SGNN-HN [112], DHCN [181]”
• Graph Self-Supervised Learning: A Survey (2021, background): “S2-MHCN [115] and DHCN [116] employ contrastive tasks for hypergraph representation learning for social- and session- based recommendation, respectively.”
• Self-Supervised Learning for Recommender Systems: A Survey (2022, methodology): “Accordingly, a flurry of enthusiasmonSSRhasalsobeenwitnessed[29],[30],[31],[32], [33].”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “DHCN [210] regards each session as a hyperedge and represents all sessions in a hypergraph to model the high-order item relations.”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “As the research of self-supervised learning is still in its infancy, there are only several works combining it with recommender systems [24, 44, 45, 64].”
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021, methodology): “An increasing number of very recent studies [28, 32, 38, 40, 44, 45] have sought to harness CL for improving recommendation performance and have demonstrated significant gains.”