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

Tianyu Zuo, Shenxin Zhu*, Jian Lu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

6 Citations (Scopus)


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 languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages16
ISBN (Print)9783030522483
Publication statusPublished - 2020
EventScience and Information Conference, SAI 2020 - London, United Kingdom
Duration: 16 Jul 202017 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1228 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceScience and Information Conference, SAI 2020
Country/TerritoryUnited Kingdom


  • Hybrid system
  • Linear mixed model
  • Singular value decomposition


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