TY - JOUR
T1 - Knowledge Discovery and Recommendation with Linear Mixed Model
AU - Chen, Zhiyi
AU - Zhu, Shengxin
AU - Niu, Qiang
AU - Zuo, Tianyu
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Knowledge discovery in database (KDD)
KW - R software
KW - linear mixed-effects model (LMM)
KW - recommender system (RS)
UR - http://www.scopus.com/inward/record.url?scp=85081643635&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2973170
DO - 10.1109/ACCESS.2020.2973170
M3 - Article
AN - SCOPUS:85081643635
SN - 2169-3536
VL - 8
SP - 38304
EP - 38317
JO - IEEE Access
JF - IEEE Access
M1 - 8993770
ER -