TY - GEN
T1 - Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation
AU - Dai, Da Ren
AU - Tsai, Richard Tzong Han
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Session-based recommendation (SBR) aims to forecast users' future actions by analyzing their unnamed behavioral sequences within a limited time frame. Recent research in SBR has focused on leveraging various techniques, including the incorporation of contrastive learning. Despite these developments, existing studies exhibit several limitations. First, these studies solely employ either item-level or session-level contrast, overlooking the vital correlation information between items and sessions. Second, to model the various relationships present in session data, numerous studies have designed complex processes to construct multiple augmented views, which diminish the accessibility of graph contrastive learning in SBR. To overcome these challenges, we propose Tri-Rec (Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation), a model that innovatively incorporates tri-directional contrast into SBR. Tri- directional contrast consists of three distinct contrastive forms, with the aim of maximizing the similarity: (1) between the same item, (2) between the same session, and (3) between each session and its containing items in augmented views. Contrary to many prevailing methods that solely employ either item-level or session-level contrast, we not only utilize both but also introduce membership-level contrast, allowing the model to harness more comprehensive information. Furthermore, we integrate the hypergraph neural network and a self-attention based readout module to capture both high-order relationships and representative user intent among sessions. Detailed empirical evaluations conducted on three real-world datasets reveal that Tri-Rec markedly surpasses state-of-the-art approaches in performance.
AB - Session-based recommendation (SBR) aims to forecast users' future actions by analyzing their unnamed behavioral sequences within a limited time frame. Recent research in SBR has focused on leveraging various techniques, including the incorporation of contrastive learning. Despite these developments, existing studies exhibit several limitations. First, these studies solely employ either item-level or session-level contrast, overlooking the vital correlation information between items and sessions. Second, to model the various relationships present in session data, numerous studies have designed complex processes to construct multiple augmented views, which diminish the accessibility of graph contrastive learning in SBR. To overcome these challenges, we propose Tri-Rec (Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation), a model that innovatively incorporates tri-directional contrast into SBR. Tri- directional contrast consists of three distinct contrastive forms, with the aim of maximizing the similarity: (1) between the same item, (2) between the same session, and (3) between each session and its containing items in augmented views. Contrary to many prevailing methods that solely employ either item-level or session-level contrast, we not only utilize both but also introduce membership-level contrast, allowing the model to harness more comprehensive information. Furthermore, we integrate the hypergraph neural network and a self-attention based readout module to capture both high-order relationships and representative user intent among sessions. Detailed empirical evaluations conducted on three real-world datasets reveal that Tri-Rec markedly surpasses state-of-the-art approaches in performance.
UR - http://www.scopus.com/inward/record.url?scp=85204961506&partnerID=8YFLogxK
U2 - 10.1109/IJCNN60899.2024.10651241
DO - 10.1109/IJCNN60899.2024.10651241
M3 - 會議論文篇章
AN - SCOPUS:85204961506
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Joint Conference on Neural Networks, IJCNN 2024
Y2 - 30 June 2024 through 5 July 2024
ER -