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Recommender systems algorithm selection using machine learning

Pimenidis, Elias; Kapetanakis, Stelios; Polatidis, Nikolaos

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Authors

Stelios Kapetanakis

Nikolaos Polatidis



Abstract

This article delivers a methodology for recommender system algorithm selection using a machine learning classifier. Initially, statistical data from real 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 da-taset has been developed a classifier can be applied to predict which recom-mender system among a range of algorithms will predict better for a given da-taset. The experimental evaluation shows that tree-based approaches 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 rec-ommender system algorithm will provide better recommendations for similar da-tasets.

Citation

Pimenidis, E., Kapetanakis, S., & Polatidis, N. (2021). Recommender systems algorithm selection using machine learning. In Proceedings of the 22nd Engineering Applications of Neural Networks Conference (477-487). https://doi.org/10.1007/978-3-030-80568-5_39

Conference Name 22nd International Conference on Engineering Applications of Neural Networks (EANN 2021)
Conference Location Crete, Greece
Start Date Jun 25, 2021
End Date Jun 27, 2021
Acceptance Date Apr 18, 2021
Online Publication Date Jul 1, 2021
Publication Date Jul 1, 2021
Deposit Date May 17, 2021
Publicly Available Date Mar 29, 2024
Publisher Springer
Pages 477-487
Series Title Proceedings of the International Neural Networks Society
Series Number 3
Series ISSN 2661-8141
Book Title Proceedings of the 22nd Engineering Applications of Neural Networks Conference
ISBN 9783030805678
DOI https://doi.org/10.1007/978-3-030-80568-5_39
Keywords Recommender Systems; Datasets; Meta recommender; Algorithm selection, Machine learning
Public URL https://uwe-repository.worktribe.com/output/7336949
Publisher URL https://www.springer.com/series/16268?detailsPage=titles

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