Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns

Author:

Wang Yaqiong1ORCID,Wu Junjie2ORCID,Wu Zhiang3ORCID,Adomavicius Gediminas4ORCID

Affiliation:

1. Leavey School of Business, Santa Clara University, Santa Clara, California 95053;

2. School of Economics and Management, Ministry of Industry and Information Technology Key Laboratory of Data Intelligence and Management, Beihang University, Beijing 100191, China;

3. School of Computer Science, Nanjing Audit University, Nanjing 210017, China;

4. 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. 1 History: Accepted by Ramaswamy Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031001, 72072091, 72242101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0194 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0194 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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