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所属单位：经济与管理学院

发表刊物：Lect. Notes Comput. Sci.

摘要：A traditional collaborative filtering recommendation algorithm has problems with data sparseness, a cold start and new users. With the rapid development of social network and e-commerce, building the trust between users and user interest tags to provide a personalized recommendation is becoming an important research issue. In this study, we propose a probability matrix factorization model (STUIPMF) by integrating social trust and user interest. First, we identified implicit trust relationship between users and potential interest label from the perspective of user rating. Then, we used a probability matrix factorization model to conduct matrix decomposition of user ratings information, user trust relationship, and user interest label information, and further determined the user characteristics to ease data sparseness. Finally, we used an experiment based on the Epinions website’s dataset to verify our proposed method. The results show that the proposed method can improve the recommendation’s accuracy to some extent, ease a cold start and solve new user problems. Meanwhile, the STUIPMF approach, we propose, also has a good scalability. © Springer International Publishing AG, part of Springer Nature 2018.

ISSN号：0302-9743

是否译文：否

发表时间：2018-01-01

合写作者：彭鹏,Mierzwiak, Rafa&lstrok

通讯作者：米传民