Deep Recommendation Model Combining Long-and Short-Term Interest Preferences

作     者：Niu, Lushuai Peng, Yan Liu, Yimao 

作者机构：Sichuan Univ Sci & Engn Coll Automat & Informat Engn Yibin Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：166455-166464页

核心收录：

基　　金：Key Research and Development Project of Science and Technology Department of Sichuan Province [19ZDYF1078] Science and Technology Plan of Zigong Science and Technology Bureau [2018GYCX33] Key Laboratory of Enterprise Informationization and Internet of Things Measurement and Control Technology in Sichuan Province Universities [2021WYJ04] 

主　　题：Mathematical models Logic gates Data mining Data models Predictive models Licenses Context modeling Recommendation algorithm self-attention mechanism bidirectional gating cyclic network sequence recommendation deep learning 

摘      要：The existing sequential recommendation algorithms cannot effectively capture and solve the problems such as the dynamic preferences of users over time. This paper proposes a deep Recommendation model CLSR (Combines Long-term and Short-term interest Recommendation) that Combines long-term and short-term interest preferences. Firstly, the model models the potential feature representation of users and items, and uses the self-attention mechanism to capture the relationship between items in the interaction of users historical behavior, so as to better learn the short-term interest representation of users. At the same time, the BiGRU network is used to extract the features of users long-term interests on a deep level. Finally, the features of long-term and short-term interest are fused. On four publicly available datasets, experimental results show that the proposed method has better improvement on HR@N, NDCG@N and MRR@N, which validates the effectiveness of the model.