期刊
INFORMATION & MANAGEMENT
卷 52, 期 7, 页码 789-800出版社
ELSEVIER
DOI: 10.1016/j.im.2015.02.004
关键词
Personalized recommendations; Recommender system; Dynamic user interests; Overlapping community; Time-weighted association rules
资金
- National Science Fund for Distinguished Young Scholars of China [70925005]
- General Program of National Science Foundation of China [71471127, 71371135, 71001076, 71101103, 71271149]
- JP Morgan Chase Fellowship from Institute for Financial Services Analytics at University of Delaware
Capturing and understanding user interests are an important part of social media analytics. Users of social media sites often belong to multiple interest communities, and their interests are constantly changing overtime. Therefore, modeling and predicting dynamic user interests poses great challenges to providing personalized recommendations in social media analytics research. We propose a novel solution to this research problem by developing a temporal overlapping community detection method based on time-weighted association rule mining. We conducted experiments using MovieLens and Netflix datasets, and our experimental results show that our proposed approach outperforms several existing methods in recommendation precision and diversity. (C) 2015 Elsevier B.V. All rights reserved.
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