A switching multi-level method for the long tail recommendation problem

Alshammari, Gharbi; Jorro-Aragoneses, Jose L.; Polatidis, Nikolaos; Kapetanakis, Stelios; Pimenidis, Elias; Petridis, Miltos

Abstract

© 2019 - IOS Press and the authors. All rights reserved. Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users' rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail.

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: May 9, 2019
• Online Publication Date: Jul 15, 2019
• Publication Date: Dec 23, 2019
• Deposit Date: May 29, 2019
• Publicly Available Date: Jun 3, 2019
• Journal: Journal of Intelligent and Fuzzy Systems
• Print ISSN: 1064-1246
• Publisher: IOS Press
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Issue: 6
• Pages: 7189-7198
• DOI: https://doi.org/10.3233/JIFS-179331
• Keywords: recommender systems, collaborative filtering, switching, multi-level, long tail recommendations
• Public URL: https://uwe-repository.worktribe.com/output/847155
• Publisher URL: https://www.iospress.nl/journal/journal-of-intelligent-fuzzy-systems/
• Contract Date: May 29, 2019