@inproceedings{580f815559304c14bbda0ebcb5e42997,
title = "Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4",
abstract = "Quantitative analysis of high throughput movie rating data provides supports for one general social behavior: the young are usually more censorious than senior people when rating/evaluating the same thing. Millions of movie rating data with users' categorical age information are analyzed by the linear mixed model with the lme4 R package. When the age factor is viewed as fixed effects, the rating scores for movies are positively related to age. In general the young people are tends to give lower score than senior people. Such a social behavior phenomenon should be carefully examined in a recommendation system and in data collection.",
keywords = "Knowledge discovery in databases(KDD), linear-mixed effects model(LMM), lme4 software, recommender system (RS)",
author = "Zhiyi Chen and Shengxin Zhu and Qiang Niu and Xin Lu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",
year = "2019",
month = may,
day = "10",
doi = "10.1109/ICBDA.2019.8713193",
language = "English",
series = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "32--36",
booktitle = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
}