Learning social regularized user representation in recommender system

作者：Guan, Jian-sheng^*; Xu, Min; Kong, Xiang-song

来源：Signal Processing, 2018, 144: 306-310.

DOI：10.1016/j.sigpro.2017.09.015

• Social network regularization
• Deep embedding
• Recommender system
• Neural language model

Appropriate user and item representation learning is critical to a successful recommender system. A set of models from natural language processing domain, namely neural language models, have recently been utilized to user and item representation learning in standard recommendation tasks. This paper proposes to exploit neural language models in the social recommendation task. Unlike previous studies that focus on modeling the user-item interaction matrix and only consider the item-level context, this paper models user social relationship information and adds an additional layer to incorporate user-level context. The derived representation is very like the social regularization imposed in matrix factorization-based recommendation, but with more flexible context, Experiments on a Douban movie dataset validate the advantage of the proposed model.

• 出版日期2018-3
• 单位厦门理工学院; 厦门大学