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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