Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4

Zhiyi Chen, Shengxin Zhu, Qiang Niu, Xin Lu

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

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-36
Number of pages5
ISBN (Electronic)9781728112824
DOIs
Publication statusPublished - 10 May 2019
Event4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
Duration: 15 Mar 201918 Mar 2019

Publication series

Name2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

Conference4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Country/TerritoryChina
CitySuzhou
Period15/03/1918/03/19

Keywords

  • Knowledge discovery in databases(KDD)
  • linear-mixed effects model(LMM)
  • lme4 software
  • recommender system (RS)

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