1.CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2.University of Chinese Academy of Sciences, Beijing 100190, China; 3.China Information Technology Security Evaluation Center, Beijing 100085, China
Abstract：The news recommender system is a popular research issue, in which the cold-start problem and the rich semantic information in the content challenges classical models. This paper proposes a collaborative joint embedding model to learn user and document vector with semantic information simultaneously. Specifically, it combines the word&doc embedding model with matrix factorization based collaborative filter mode. Experiment on real-world dataset shows that the proposed model outperforms other baseline models.
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2018, Vol. 32 
