Sequential recommendation method integrating item relationship and user preference

Abstract

The sequential recommendation aims to recommend items that may be of interest to users based on user behavior sequence information. However, most of the current sequential recommendation tasks cannot adequately model the longterm preferences and short-term intentions of users when mining user preferences. In order to model and integrate the user's long-term and short-term preferences effectively, a new sequential recommendation method, which integrates item relationship and user preference, named IRUP, is proposed. Firstly, Graph Convolution Network (GCN) and an itemrelation level attention mechanism are utilized to model long-term and short-term preferences of users respectively; secondly, a co-attention mechanism is used to learn the cross-correlation information between long-term and short-term preferences, which enhances the user's preference representation; finally, the long-term preference and the short-term preference are fused, and the inner product is introduced to calculate the recommendation list. The experimental results on two public datasets Beauty and Home show that the proposed method IRUP can effectively improve the recommendation performance. Compared with the best baseline method KDA, the two evaluation metrics HR@5 and NDCG@5 have an average improvement of 8.28% and 7.03%, respectively.