关联项目增强的多兴趣序列推荐方法.

Existing sequential recommendation methods based on multi-interest frameworks only learn the multi-interest representations from users' recent interaction sequences, ignoring the association information between items in the dataset. To address this problem, this paper proposed an item associations aware multi-interest sequential recommendation method (IAMIRec). Firstly, this method obtained the item association set and matrix by calculating the user' s interaction sequence in the dataset, then modeled the user's recent interaction sequence by the association martix and the multi-head self-attention mechanism, and used the multi-interest framework to model the user's multiple interest vectors. It used the obtained multiple interest vectors to the top-N recommendation for users. This paper tested and analyzed the model on three datasets, IAMIRec outperformed related methods on recall, NDCG, and hit rate metrics. The results show that IAM!Rec can achieve better recommendation performance, and also show that it can effectively enhance the multi-interest representation of users through item associations information. [ABSTRACT FROM AUTHOR]