Predicting Movies User Ratings with Imdb Attributes

摘要：

　　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.

关键词： User rating prediction model classification linear combination convex combination neural networks multiple linear regression stepwise regression IMDb

作者: Ping-Yu Hsu Yuan-Hong Shen Xiang-An Xie

作者单位: Department of Business Administration, National Central University, Jhongli City,Taoyuan County, Taiwan(R.O.C.)

会议类型: 国际会议

会议名称: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)

会议地点: 上海

会议语种:英文

页码: 444-453

在线出版日期: 2014-10-24（万方平台首次上网日期，不代表论文的发表时间）