Modeling Tag-Aware Recommendations Based on User Preferences

Jiajin Hunag, Xi Yuan, Ning Zhong () and Yiyu Yao
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Jiajin Hunag: International WIC Institute, Beijing University of Technology, Beijing, China
Xi Yuan: International WIC Institute, Beijing University of Technology, Beijing, China
Ning Zhong: International WIC Institute, Beijing University of Technology, Beijing, China;
Yiyu Yao: International WIC Institute, Beijing University of Technology, Beijing, China;

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 05, 947-970

Abstract: A recommender system aims at recommending items that users might be interested in. With an increasing popularity of social tagging systems, it becomes urgent to model recommendations on users, items, and tags in a unified way. In this paper, we propose a framework for studying recommender systems by modeling user preferences as a relation on (user, item, tag) triples. We discuss tag-aware recommender systems from two aspects. On the one hand, we compute associations between users and items related to tags by using an adaptive method and recommend tags to users or predict item properties for users. On the other hand, by taking the similarity-based recommendation as a case study, we discuss similarity measures from both qualitative and quantitative perspectives andk-nearest neighbors and reversek-nearest neighbors for recommendations.

Keywords: Tag; recommender system; preference relation (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0219622015500194

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