Predicting movies user ratings with IMDb attributes

Ping Yu Hsu, Yuan Hong Shen, Xiang An Xie

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

8 Scopus citations

Abstract

In the era of Web 2.0, consumers share their ratings or comments easily with other people after watching a movie. User rating simplified the procedure which consumers express their opinions about a product, and is a great indicator to predict the box office [1-4]. This study develops user rating prediction models which used classification technique (linear combination, multiple linear regression, neural networks) to develop. Total research dataset included 32968 movies, 31506 movies were training data, and others were testing data. Three of research findings are worth summarizing: first, the prediction absolute error of three models is below 0.82, it represents the user ratings are well-predicted by the models; second, the forecast of neural networks prediction model is more accurate than others; third, some predictors profoundly affect user rating, such as writers, actors and directors. Therefore, investors and movie production companies could invest an optimal portfolio to increase ROI.

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology - 9th International Conference, RSKT 2014, Proceedings
EditorsDuoqian Miao, Georg Peters, Qinghua Hu, Ruizhi Wang, Duoqian Miao, Georg Peters, Witold Pedrycz, Dominik Ślęzak
PublisherSpringer Verlag
Pages444-453
Number of pages10
ISBN (Electronic)9783319117393
DOIs
StatePublished - 2014
Event9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014 - Shanghai, China
Duration: 24 Oct 201426 Oct 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8818
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014
Country/TerritoryChina
CityShanghai
Period24/10/1426/10/14

Keywords

  • Classification
  • Convex combination
  • IMDb
  • Linear combination
  • Multiple linear regression
  • Neural networks
  • Prediction model
  • Stepwise regression
  • User rating

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