Title | Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4 |
Creator | |
Date Issued | 2019-05-10 |
Conference Name | 4th IEEE International Conference on Big Data Analytics (ICBDA) |
Source Publication | 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
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Pages | 32-36 |
Conference Date | MAR 15-18, 2019 |
Conference Place | Suzhou |
Country | PEOPLES 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. |
Keyword | Knowledge discovery in databases(KDD) linear-mixed effects model(LMM) lme4 software recommender system (RS) |
DOI | 10.1109/ICBDA.2019.8713193 |
URL | View source |
Indexed By | CPCI-S |
Language | 英语English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science ; Information Systems ; Computer Science ; Software EngineeringComputer Science, Theory & Methods |
WOS ID | WOS:000469958800007 |
Scopus ID | 2-s2.0-85066604329 |
Citation statistics | |
Document Type | Conference paper |
Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11505 |
Collection | Research 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. |
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