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Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns

Author

Listed:
  • Yaqiong Wang

    (Leavey School of Business, Santa Clara University, Santa Clara, California 95053)

  • Junjie Wu

    (School of Economics and Management, Ministry of Industry and Information Technology Key Laboratory of Data Intelligence and Management, Beihang University, Beijing 100191, China)

  • Zhiang Wu

    (School of Computer Science, Nanjing Audit University, Nanjing 210017, China)

  • Gediminas Adomavicius

    (Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

With the increasing use of recommender systems in various application domains, many algorithms have been proposed for improving the accuracy of recommendations. Among various dimensions of recommender systems performance, long-tail (niche) recommendation performance remains an important challenge in large part because of the popularity bias of many existing recommendation techniques. In this study, we propose CORE, a cosine pattern–based technique, for effective long-tail recommendation. Comprehensive experimental results compare the proposed approach with a wide variety of classic, widely used recommendation algorithms and demonstrate its practical benefits in accuracy, flexibility, and scalability in addition to the superior long-tail recommendation performance.

Suggested Citation

  • Yaqiong Wang & Junjie Wu & Zhiang Wu & Gediminas Adomavicius, 2025. "Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns," INFORMS Journal on Computing, INFORMS, vol. 37(2), pages 446-464, March.
    • Handle: RePEc:inm:orijoc:v:37:y:2025:i:2:p:446-464
    DOI: 10.1287/ijoc.2022.0194
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