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Latent Factor Analysis for High-dimensional and Sparse Matrices

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-19-6703-0

ISBN: 978-981-19-6702-3

Publisher: Springer Nature Singapore - Springer Journals

Nov 16, 2022

Available

Introduction

Yuan, Ye; Luo, Xin

Pages: 1 –10

[With the rapid development of basic computing and storage facilities, the big-data-related industrial applications, i.e., recommendation system [1–5], cloud services [6–10], social networks [11–16], and wireless sensor networks [17–22], continue to expand. Consequently, the data involved in...
Available

Learning Rate-Free Latent Factor Analysis via PSO

Yuan, Ye; Luo, Xin

Pages: 11 –27

[With the explosive growth of the Internet, data generated by numerous industrial applications grows exponentially. Therefore, it is difficult for people to obtain useful information from big data, which contain a wealth of knowledge and are high-dimensional and sparse (HiDS) [1–5], e.g., node...
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Learning Rate and Regularization Coefficient-Free Latent Factor Analysis via PSO

Yuan, Ye; Luo, Xin

Pages: 29 –49

[A big-data-related application commonly has numerous nodes, e.g., users and items in a recommender system [1–5]. With the exponential growth of involved nodes, it is impossible to obtain their whole interaction relationships, e.g., it is impossible for a recommender system’s individual user to...
Available

Regularization and Momentum Coefficient-Free Non-negative Latent Factor Analysis via PSO

Yuan, Ye; Luo, Xin

Pages: 51 –74

[A big-data-related application [1–11] commonly involves numerous nodes with the inherent non-negativity interaction relationships, i.e., user-item interactions in a recommender system [12–14]. Due to the exponential growth of involved nodes, it is impossible to obtain their whole interaction...
Available

Advanced Learning Rate-Free Latent Factor Analysis via P2SO

Yuan, Ye; Luo, Xin

Pages: 75 –90

[Numerous entities are frequently encountered in various big data-related applications like wireless sensor networks [1–3], bioinformatics applications [4–9], social networks [10–13], user-service QoS [14, 15] and electronic commerce systems [16–18]. With the increasing number of involved...
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Conclusion and Future Directions

Yuan, Ye; Luo, Xin

Pages: 91 –92

[This book is aiming at advancing latent factor analysis for high-dimensional and sparse matrices. In particular, we mainly introduce how to incorporate the principle of particle swarm optimization into latent factor analysis, thereby implementing effective hyper-parameter adaptation.]

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