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- W3153714665 abstract "Variational autoencoder is a very concise and effective unsupervised learning method, which can achieve excellent performance when applied in the field of recommendation systems. At present, most of the research on recommendation algorithms consider the user's social relationship, and rarely consider the social relationship between items. Focused on the problem of how to improve the recommendation accuracy of the recommendation system under the condition of sparse scoring data, a recommendation algorithm (SR-CVAE) for fusion item social relationship regularization and variational encoder are proposed. In this paper, the user-item feedback information, item content information, social relationship between items, and tag information are combined, and integrated into a variational encoder with a hidden variable generation model, and then combing the variational encoder with the discriminant model and applying it to the collaborative filtering recommendation algorithm to improve the accuracy of the algorithm. Experimental results on two CiteULike datasets show that the proposed algorithm has better interpretation and recommendation performance than other models." @default.
- W3153714665 created "2021-04-26" @default.
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- W3153714665 date "2021-03-12" @default.
- W3153714665 modified "2023-09-27" @default.
- W3153714665 title "Research on recommendation algorithm combining item social regularization and variational encoder" @default.
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- W3153714665 doi "https://doi.org/10.1109/iaeac50856.2021.9390988" @default.
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