Recommender systems algorithm selection using machine learning

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Abstract

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
Publication statusPublished - 2021

Publication series

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

Keywords

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

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