Dr Elias Pimenidis Elias.Pimenidis@uwe.ac.uk
Senior Lecturer in Computer Science
Recommender systems algorithm selection using machine learning
Pimenidis, Elias; Kapetanakis, Stelios; Polatidis, Nikolaos
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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