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Personalized movie recommendations based on deep representation learning

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-i...

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Detalles Bibliográficos
Autores principales: Li, Luyao, Huang, Hong, Li, Qianqian, Man, Junfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403217/
https://www.ncbi.nlm.nih.gov/pubmed/37547384
http://dx.doi.org/10.7717/peerj-cs.1448
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author Li, Luyao
Huang, Hong
Li, Qianqian
Man, Junfeng
author_facet Li, Luyao
Huang, Hong
Li, Qianqian
Man, Junfeng
author_sort Li, Luyao
collection PubMed
description Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.
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spelling pubmed-104032172023-08-05 Personalized movie recommendations based on deep representation learning Li, Luyao Huang, Hong Li, Qianqian Man, Junfeng PeerJ Comput Sci Artificial Intelligence Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model. PeerJ Inc. 2023-07-12 /pmc/articles/PMC10403217/ /pubmed/37547384 http://dx.doi.org/10.7717/peerj-cs.1448 Text en ©2023 Li 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Li, Luyao
Huang, Hong
Li, Qianqian
Man, Junfeng
Personalized movie recommendations based on deep representation learning
title Personalized movie recommendations based on deep representation learning
title_full Personalized movie recommendations based on deep representation learning
title_fullStr Personalized movie recommendations based on deep representation learning
title_full_unstemmed Personalized movie recommendations based on deep representation learning
title_short Personalized movie recommendations based on deep representation learning
title_sort personalized movie recommendations based on deep representation learning
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403217/
https://www.ncbi.nlm.nih.gov/pubmed/37547384
http://dx.doi.org/10.7717/peerj-cs.1448
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