Abstract Session-based recommendation(SBR) aims to provide recommendations for anonymous users or users who are not logged in based on data in the session. The existing research models a single item in the session as the smallest unit, ignoring the item representation in different receptive fields. Moreover, the implicit topic information contained in the session sequence is not mined. To alleviate these issues, a topic-enhanced multi-level graph neural network(TEMGNN) for SBR is proposed. Firstly, a multi-level item embedding learning module is designed to broaden the receptive fields of item and obtain the representation of items at different granularities. Then, the proposed multi-level graph neural network is employed to propagate the item information with and cross granularities, capturing richer item embedding representation. Furthermore, a topic learning module is proposed to extract the topic commonalities of items in hidden space and automatically form topic representations of items by explicit vector space projection without relying on any item attribute information. Thus, the recommendation performance of the model is enhanced. Experiments on three benchmark datasets show the superiority of TEMGNN.
Fund:National Natural Science Foundation of China(No.62141201), Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-MSX1672), Major Project of Science and Technology Research Program of Chongqing Education Commission of China(No.KJZD-M202201102)
Corresponding Authors:
ZHU Xiaofei, Ph.D., professor. His research interests include natural language processing, data mining and information retrieval.
About author:: TANG Gu, master student. His research interests include recommendation systems and natural language processing.
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2023, Vol. 36 
