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Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems
A Multi-Criteria Recommender System (MCRS) represents users’ preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective rec...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368822/ https://www.ncbi.nlm.nih.gov/pubmed/37501952 http://dx.doi.org/10.1016/j.heliyon.2023.e18183 |
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author | Wasid, Mohammed Ali, Rashid Shahab, Sana |
author_facet | Wasid, Mohammed Ali, Rashid Shahab, Sana |
author_sort | Wasid, Mohammed |
collection | PubMed |
description | A Multi-Criteria Recommender System (MCRS) represents users’ preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures. |
format | Online Article Text |
id | pubmed-10368822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103688222023-07-27 Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems Wasid, Mohammed Ali, Rashid Shahab, Sana Heliyon Research Article A Multi-Criteria Recommender System (MCRS) represents users’ preferences on several factors of products and utilizes these preferences while making product recommendations. In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains. However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product. Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS. Initially, we build a model by assigning weights to multi-criteria features and then learn the preferences on each criteria during similarity computation among users through a genetic algorithm. This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making. Finally, products are recommended after making predictions. The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures. Elsevier 2023-07-12 /pmc/articles/PMC10368822/ /pubmed/37501952 http://dx.doi.org/10.1016/j.heliyon.2023.e18183 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Wasid, Mohammed Ali, Rashid Shahab, Sana Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title | Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title_full | Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title_fullStr | Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title_full_unstemmed | Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title_short | Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
title_sort | adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368822/ https://www.ncbi.nlm.nih.gov/pubmed/37501952 http://dx.doi.org/10.1016/j.heliyon.2023.e18183 |
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