Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns

Yaqiong Wang (), Junjie Wu (), Zhiang Wu () and Gediminas Adomavicius ()
Additional contact information
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

INFORMS Journal on Computing, 2025, vol. 37, issue 2, 446-464

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.

Keywords: recommender systems; pattern-based recommendation; cosine patterns; long-tail recommendation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2022.0194 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:2:p:446-464

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().