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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


Gharbi Alshammari

Jose L. Jorro-Aragoneses

Nikolaos Polatidis

Stelios Kapetanakis

Miltos Petridis


© 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.


Alshammari, G., Jorro-Aragoneses, J. L., Polatidis, N., Kapetanakis, S., Pimenidis, E., & Petridis, M. (2019). A switching multi-level method for the long tail recommendation problem. Journal of Intelligent and Fuzzy Systems, 37(6), 7189-7198.

Journal Article Type Conference Paper
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
Electronic ISSN 1875-8967
Publisher IOS Press
Peer Reviewed Peer Reviewed
Volume 37
Issue 6
Pages 7189-7198
Keywords recommender systems, collaborative filtering, switching, multi-level, long tail recommendations
Public URL
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