Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation
PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023(2023)
Abstract
Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to utilize information other than the current session and how to reduce the negative impact of irrelevant information in the session data on the prediction. To address these challenges, we propose a novel graph attention network-based model called Multiple Sparse Graph Attention Networks (MSGAT). MSGAT leverages two parallel channels to model intra-session and inter-session information. In the intrasession channel, we utilize a gated graph neural network to perform initial encoding, followed by a self-attention mechanism to generate the target representation. The global representation is then noise-reduced based on the target representation. Additionally, the target representation is used as a medium to connect the two channels. In the inter-session channel, the noise-reduced relation representation is generated using the global attention mechanism of target perception. Moreover, MSGAT fully considers session similarity from the intent perspective by integrating valid information from both channels. Finally, the intent neighbor collaboration module effectively combines relevant information to enhance the current session representation. Extensive experiments on five datasets demonstrate that simultaneous modeling of intra-session and inter-session data can effectively enhance the performance of the SBR model.
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Key words
Session-based Recommendation,Multiple Sparse Graph Attention Networks,Graph Neural Networks
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