Details of Research Outputs

TitleCensorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4
Creator
Date Issued2019-05-10
Conference Name4th IEEE International Conference on Big Data Analytics (ICBDA)
Source Publication2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Pages32-36
Conference DateMAR 15-18, 2019
Conference PlaceSuzhou
CountryPEOPLES R CHINA
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.

KeywordKnowledge discovery in databases(KDD) linear-mixed effects model(LMM) lme4 software recommender system (RS)
DOI10.1109/ICBDA.2019.8713193
URLView source
Indexed ByCPCI-S
Language英语English
WOS Research AreaComputer Science
WOS SubjectComputer Science ; Information Systems ; Computer Science ; Software EngineeringComputer Science, Theory & Methods
WOS IDWOS:000469958800007
Scopus ID2-s2.0-85066604329
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/11505
CollectionResearch outside affiliated institution
Affiliation
Department of Mathematics,Xi'An Jiaotong-Liverpool University,China
Recommended Citation
GB/T 7714
Chen, Zhiyi,Zhu, Shengxin,Niu, Qianget al. Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4[C], 2019: 32-36.
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Chen, Zhiyi]'s Articles
[Zhu, Shengxin]'s Articles
[Niu, Qiang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Zhiyi]'s Articles
[Zhu, Shengxin]'s Articles
[Niu, Qiang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Zhiyi]'s Articles
[Zhu, Shengxin]'s Articles
[Niu, Qiang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.