Recommender systems algorithm selection using machine learning

Nikolaos Polatidis, Stelios Kapetanakis, Elias Pimenidis

    Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


    This article delivers a methodology for recommender system algorithm se-lection using a machine learning classifier. Initially, statistical data from re-al collaborative filtering recommender systems have been collected to form the basis for a synthetic dataset since a real meta dataset doesn’t exist. Once the dataset has been developed a classifier can be applied to predict which recommender system among a range of algorithms will predict better for a given dataset. The experimental evaluation shows that tree-based approach-es such as Decision Tree and Random Forest work well and provide results with high accuracy and precision. We can conclude that machine learning can be used along with a meta dataset comprised of statistical information in order to predict which recommender system algorithm will provide better recommendations for similar datasets.
    Original languageEnglish
    Title of host publication22nd International Conference on Engineering Applications of Neural Networks
    Place of PublicationCham
    ISBN (Electronic)9783030805685
    ISBN (Print)9783030805678
    Publication statusPublished - 1 Jul 2021

    Publication series

    NameProceedings of the International Neural Networks Society
    ISSN (Print)2661-8141


    • Recommender Systems
    • Datasets
    • Meta recommender
    • Algorithm selection
    • Machine learning


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