Titlebook: Latent Factor Analysis for High-dimensional and Sparse Matrices; A particle swarm opt Ye Yuan,Xin Luo Book 2022 The Author(s), under exclus

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2191-5768 atent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed..978-981-19-6702-3978-981-19-6703-0Series ISSN 2191-5768 Series E-ISSN 2191-5776

使人入神

Learning Rate-Free Latent Factor Analysis via PSO,ction in sensor networks [6–8], user-service invoking in cloud computing [9–15], protein interaction in biological information [16–18], user interactions in social networks service systems [19–21], and user-item preferences in recommender systems [22–25].

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2191-5768 tation method for latent factor analysis models.Outlines an Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor an

FUSE

Introduction,ic relationships among entities. For instance, the Douban matrix [32] collected by the Chinese largest online book, movie and music database includes 129,490 users and 58,541 items. However, it only contains 16,830,839 known ratings and the density is 0.22%.

Estimable

Ye Yuan,Xin LuoOffers a comprehensive introduction to latent factor analysis on high-dimensional and sparse data.Presents an effective hyper-parameter adaptation method for latent factor analysis models.Outlines an