A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model

Tianyu Zuo; Shenxin Zhu; Jian Lu

doi:10.1007/978-3-030-52249-0_25

A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model

Tianyu Zuo, Shenxin Zhu^*, Jian Lu

^*Corresponding author for this work

School of Mathematics and Physics

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

7 Citations (Scopus)

Abstract

We explain the basic idea of linear mixed model (LMM), including parameter estimation and model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is also mentioned. After introducing the related R packages to implement these two models, we compare the mean absolute errors of LMM and SVD when different numbers of historical ratings are given. Then a hybrid recommender system is developed to combine these two models together. Such system is proven to have higher accuracy than single LMM and SVD model. And it might have practical value in different fields in the future.

Original language	English
Title of host publication	Intelligent Computing - Proceedings of the 2020 Computing Conference
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer
Pages	347-362
Number of pages	16
ISBN (Print)	9783030522483
DOIs	https://doi.org/10.1007/978-3-030-52249-0_25
Publication status	Published - 2020
Event	Science and Information Conference, SAI 2020 - London, United Kingdom Duration: 16 Jul 2020 → 17 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1228 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Science and Information Conference, SAI 2020
Country/Territory	United Kingdom
City	London
Period	16/07/20 → 17/07/20

Keywords

Hybrid system
Linear mixed model
Singular value decomposition

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10.1007/978-3-030-52249-0_25

Cite this

Zuo, T., Zhu, S., & Lu, J. (2020). A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Computing - Proceedings of the 2020 Computing Conference (pp. 347-362). (Advances in Intelligent Systems and Computing; Vol. 1228 AISC). Springer. https://doi.org/10.1007/978-3-030-52249-0_25

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title = "A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model",

abstract = "We explain the basic idea of linear mixed model (LMM), including parameter estimation and model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is also mentioned. After introducing the related R packages to implement these two models, we compare the mean absolute errors of LMM and SVD when different numbers of historical ratings are given. Then a hybrid recommender system is developed to combine these two models together. Such system is proven to have higher accuracy than single LMM and SVD model. And it might have practical value in different fields in the future.",

keywords = "Hybrid system, Linear mixed model, Singular value decomposition",

author = "Tianyu Zuo and Shenxin Zhu and Jian Lu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Science and Information Conference, SAI 2020 ; Conference date: 16-07-2020 Through 17-07-2020",

year = "2020",

doi = "10.1007/978-3-030-52249-0_25",

language = "English",

isbn = "9783030522483",

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Zuo, T, Zhu, S & Lu, J 2020, A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Computing - Proceedings of the 2020 Computing Conference. Advances in Intelligent Systems and Computing, vol. 1228 AISC, Springer, pp. 347-362, Science and Information Conference, SAI 2020, London, United Kingdom, 16/07/20. https://doi.org/10.1007/978-3-030-52249-0_25

A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model. / Zuo, Tianyu; Zhu, Shenxin; Lu, Jian.
Intelligent Computing - Proceedings of the 2020 Computing Conference. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2020. p. 347-362 (Advances in Intelligent Systems and Computing; Vol. 1228 AISC).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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N2 - We explain the basic idea of linear mixed model (LMM), including parameter estimation and model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is also mentioned. After introducing the related R packages to implement these two models, we compare the mean absolute errors of LMM and SVD when different numbers of historical ratings are given. Then a hybrid recommender system is developed to combine these two models together. Such system is proven to have higher accuracy than single LMM and SVD model. And it might have practical value in different fields in the future.

AB - We explain the basic idea of linear mixed model (LMM), including parameter estimation and model selection criteria. Moreover, the algorithm of singular value decomposition (SVD) is also mentioned. After introducing the related R packages to implement these two models, we compare the mean absolute errors of LMM and SVD when different numbers of historical ratings are given. Then a hybrid recommender system is developed to combine these two models together. Such system is proven to have higher accuracy than single LMM and SVD model. And it might have practical value in different fields in the future.

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KW - Singular value decomposition

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T2 - Science and Information Conference, SAI 2020

Y2 - 16 July 2020 through 17 July 2020

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A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Model

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