Knowledge Discovery and Recommendation With Linear Mixed Model

IEEE Access

Qiang Niu¹  Zhiyi Chen¹  Tianyu Zuo²  Shengxin Zhu² 

[1] Department of Mathematical Science, Xi&x2019;an Jiaotong-Liverpool University, Suzhou, China;

关键词: Knowledge discovery in database (KDD);    linear mixed-effects model (LMM);    recommender system (RS);    R software;   

DOI  :  10.1109/ACCESS.2020.2973170

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown