Hypergraph Convolutional Networks for User Micro-Behavior Session-Based Recommendation

Session-based recommender system（SBRS）is widely concerned by industry and academia, because it only uses short-term session information of users to make recommendations without using user profile and long-term history information. When modeling user session information, the existing session-based recommendation system constructs each session as an independent graph, ignores the correlation between items, only considers the single user commodity interaction information, and ignores the diversity of interactions（such as browsing, clicking, adding to shopping cart, etc.）. To solve the above problems, this paper proposes a user micro-behavior session-based recommendation method based on hypergraph convolution network. The method first constructs the interaction sequence between users and items as a hypergraph to learn the high-order correlation between items in the session, and uses the hypergraph convolution neural network to get the embedding of the interactive product sequence. Then, a series of operations generated when users interact with goods are represented asmicro-behavior sequenceto enrich the diversity of interaction, and the embedding of micro-behavior sequence is obtained by using gated recurrent unit（GRU）network learning. Finally, the two are fused together to obtain a more fine-grained embedded representation for the SBRS. A large number of experiments in data sets Tmall and JDATA show that, compared with the baseline method, the recommendation accuracy of rating indicators P@20 and MRR@20 has been significantly improved. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.