计算机科学
推荐系统
机器学习
人工智能
半监督学习
监督学习
深度学习
协同过滤
无监督学习
特征学习
任务(项目管理)
作者
Renqi Jia,Xu Bai,Xiaofei Zhou,Shirui Pan
出处
期刊:International Joint Conference on Neural Network
日期:2021-07-18
卷期号:: 1-8
标识
DOI:10.1109/ijcnn52387.2021.9534405
摘要
Sequential recommendation that aims to predict user preference with historical user interactions becomes one of the most popular tasks in the recommendation area. The existing methods concentrated on user's sequential features among exposed items have achieved good performance. However, they only rely on single item prediction optimization to learn data representation, which ignores the association between context data and sequence data. In this paper, we propose a novel self-supervised learning based sequential recommendation network (SSLRN), which contrastively learns data correlation to promote data representation of users and items. We design two auxiliary contrastive learning tasks to regularize user and item representation based on mutual information maximization (MIM). In particular, the item contrastive learning captures sequential contrast feature with sequence-item MIM, and the user contrastive learning regularizes user latent representation with user-item MIM. We evaluate our model on five real-world datasets and the experimental results show that the proposed framework significantly and consistently outperforms state-of-the-art sequential recommendation techniques.
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