Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent

Fan Yang

doi:10.1016/j.asoc.2024.111406

Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent

2024-03-01

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DOI：10.1016/j.asoc.2024.111406

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Session -based recommendation aims to predict the next item an anonymous user is most likely to click based on its limited historical behaviors. However, in session -based recommendation, the anonymity and short sequences often lead to the issue of data sparsity, which can affect the performance of recommendation models. Existing recommendation methods mostly focus on individual items in the session sequence, without considering the implicit user continuous intent underlying multiple continuous items. Moreover, current graphbased recommendation models do not fully take into account the direction of information transfer among shared items when calculating their similarities. This paper proposes, TCAUIS, a topic classification augmented user intend self -supervised hypergraph neural network for session -based recommendation, which contains continuous topic intent of users within a session and the information of co -occurrence items. The TCAUIS models a individual session sequence as a topic intent hypergraph and co -occurrence graph respectively, and employs latent Dirichlet allocation to classify the topic of the dataset and fuses the topic hyperedges into the hypergraph to alleviate the sparsity of the data and enhance the session information. Additionally, the hypergraph attention networks and attention mechanisms are leveraged to capture the complex highorder information between nodes in both the hypergraphs and co -occurrence graphs. The continuous topic intent of users and information of co -occurrence relationship are effectively integrated using a self -supervised mechanism that maximizes the mutual information between the representations of two sessions. Extensive experimental results show that the proposed TCAUIS model outperforms the state-of-the-art models on three real datasets Diginetica, Yoochoose1/64 and Nowplaying.

Keywords:

Session-based recommendation

Latent Dirichlet allocation

Topic intent hypergraph

Self-supervised mechanism

Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent

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