Product and User Dependent Social Network Models for Recommender Systems
作者： Min Li , Zhiwei Jiang , Bin Luo , Jiubin Tang , Qing Gu
DOI: 10.1007/978-3-642-37456-2_2
关键词: Product (category theory) 、 Computer science 、 Context (language use) 、 Rating matrix 、 Internet users 、 World Wide Web 、 Recommender system 、 Added value 、 Data set (IBM mainframe) 、 Social network
摘要: Social network based applications such as Facebook, Myspace and LinkedIn have become very popular among Internet users, a major research problem is how to use the social information better infer users’ preferences make recommender systems. A common trend combining user-item rating matrix for recommendations. However, existing solutions add particular user without considering different characteristics of products be recommended neighbors involved. This paper proposes new approach that can adaptively utilize on context characterized by users. complements several recommendation algorithms thus integrated with solutions. Experimental results Epinions data set demonstrate added value proposed solution in two tasks: prediction top-K
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