The current advanced session recommendation algorithms mainly use graph neural network to mine the pairwise transformation relationships of items from the global and target sessions, and compress the target session into a fixed vector representation, ignoring the complex high-order information between items and the impact of target items on user preference diversity. To this end, this paper proposed a hypergraph convolution network and target multi-intention perception for session-based recommendation algorithm HCN-TMP. This algorithm expressed user preference by learning session representation. Firstly, it constructed a session graph based on the target session, and constructed a hypergraph based on the global session. It transformed the original item embedding representation that reflected the user's coupling intention into a multi factor embedding representation of the item through intention disentanglement technology. Then, it learned the item representations of the session level and global level of the target session node through graph attention network and hypergraph convolutional network respectively, and used the distance correlation loss function to enhance the independence between the multi-factor embedded blocks. Next, it embedded the node location information in the target session, weighted the attention weight of each node, and got the session representation of the global level and session level. It used comparative learning to maximize the mutual information of the two. Through the target multi-intention perception, it adaptively learned the multi-intention user preferences in the target session for different target items, obtained the session representation of the target perception level. Finally, it linearly fused the three level session representations to obtain the final session representation. This paper carried out the experiments on two public data sets, Tmall and Nowplaying. The experimental results verify the effectiveness of the HCN-TMP algorithm.

[2023-07-21] Accepted Paper /doc/2023.05.0199/v1

[2024-01-05] Printed Article /doc/2023.05.0199/v2