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FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction

Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improvi...

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Detalles Bibliográficos
Autores principales: Su, Zhan, Yang, Haochuan, Ai, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461840/
https://www.ncbi.nlm.nih.gov/pubmed/37639436
http://dx.doi.org/10.1371/journal.pone.0290622
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author Su, Zhan
Yang, Haochuan
Ai, Jun
author_facet Su, Zhan
Yang, Haochuan
Ai, Jun
author_sort Su, Zhan
collection PubMed
description Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind.
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spelling pubmed-104618402023-08-29 FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction Su, Zhan Yang, Haochuan Ai, Jun PLoS One Research Article Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind. Public Library of Science 2023-08-28 /pmc/articles/PMC10461840/ /pubmed/37639436 http://dx.doi.org/10.1371/journal.pone.0290622 Text en © 2023 Su et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Su, Zhan
Yang, Haochuan
Ai, Jun
FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title_full FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title_fullStr FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title_full_unstemmed FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title_short FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
title_sort fplv: enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461840/
https://www.ncbi.nlm.nih.gov/pubmed/37639436
http://dx.doi.org/10.1371/journal.pone.0290622
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