22nd EANN 2021, 25 - 27 June 2021, Greece

Recommender systems algorithm selection using machine learning

Nikolaos Polatidis, Stelios Kapetanakis, Elias Pimenidis


  This article delivers a methodology for recommender system algorithm selection using a machine learning classifier. Initially, statistical data from real collabora-tive 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 sys-tem among a range of algorithms will predict better for a given dataset. The ex-perimental evaluation shows that tree-based approaches such as Decision Tree and Random Forest work well and provide results with high accuracy and pre-cision. We can conclude that machine learning can be used along with a meta dataset comprised of statistical information in order to predict which recom-mender system algorithm will provide better recommendations for similar da-tasets.  

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