Knowledge Discovery and Recommendation with Linear Mixed Model

Zhiyi Chen, Shengxin Zhu*, Qiang Niu, Tianyu Zuo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed. Mixed effects models produce sound inference based on a series of rigorous analysis. In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object. Such a social behavior phenomenon can be used in recommender systems and business data analysis.

Original languageEnglish
Article number8993770
Pages (from-to)38304-38317
Number of pages14
JournalIEEE Access
Publication statusPublished - 2020


  • Knowledge discovery in database (KDD)
  • R software
  • linear mixed-effects model (LMM)
  • recommender system (RS)

Cite this