Enhanced Self-Attention Mechanism for Long and Short Term Sequential Recommendation Models
Xiaoyao Zheng, Xingwang Li, Zhenghua Chen, Liping Sun, Qingying Yu, Liangmin Guo, Yonglong Luo
Abstract
Compared with traditional recommendation algorithms based on collaborative filtering and content, the sequential recommendation can better capture changes in user interests and recommend items that may be interacted with by the next time according to the user's historical interaction behaviors. Generally, there are several traditional methods for sequential recommendation: Markov Chain (MC) and Deep Neutral Network (DNN), both of which ignore the relationship between various behaviors and the dynamic changes of user interest in items over time. Furthermore, the early research methods usually deal with the user's historical interaction behavior in chronological order, which may cause the loss of partial preference information. According to the perspective that user preferences will change over time, this paper proposes a long and short-term sequential recommendation model with the enhanced self-attention network, RP-SANRec. The short-term intent module of RP-SANRec uses the Gated Recurrent Unit (GRU) to learn the comprehensive historical interaction sequence of the user to calculate the position weight information in the time order, which can be used to enhance the input of the self-attention mechanism. The long-term module captures the user's preferences through a bidirectional long and short-term memory network (Bi-LSTM). Finally, the user's dynamic interests and general preferences are fused, and the following recommendation result is predicted. This article applies the RP-SANRec model to three different public datasets under two evaluation indicators of HR@10 and NDCG@10. The extensive experiments proved that our proposed RP-SANRec model performs better than existing models.