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Modeling Tag-Aware Recommendations Based on User Preferences

Author

Listed:
  • 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;
    Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan)

  • Yiyu Yao

    (International WIC Institute, Beijing University of Technology, Beijing, China;
    Department of Computer Science, University of Regina, Saskatchewan, Canada)

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.

Suggested Citation

  • Jiajin Hunag & Xi Yuan & Ning Zhong & Yiyu Yao, 2015. "Modeling Tag-Aware Recommendations Based on User Preferences," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(05), pages 947-970.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:05:n:s0219622015500194
    DOI: 10.1142/S0219622015500194
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