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

Gharbi Alshammari, Jose Luis Jorro Aragoneses, Nikolaos Polatidis, Stelios Kapetanakis, Elias Pimenidis, Miltos Petridis

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)7189-7198
Number of pages10
JournalJournal of Intelligent and Fuzzy Systems
Issue number6
Publication statusPublished - 23 Dec 2019

Bibliographical note

The final publication is available at IOS Press through http://dx.doi.org/10.3233/JIFS-179331


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