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.
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References
Elberse, A., Eliashberg, J.: Demand and supply dynamics for sequentially released products in International markets: The case of motion pictures. Marketing Science 22(3), 329–354 (2003)
Reinstein, D.A., Snyder, C.M.: The influence of expert reviews on consumer demand for experience goods: A case study ofmovie critics. The Journal of Industrial Economics 53(1), 27–51 (2005)
Eliashberg, J., Shugan, S.M.: Film critics: Influencers or predictors? Journal of Marketing 61(2), 68–78 (1997)
Basuroy, S., Chatterjee, S., Ravid, S.A.: How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing 67(4), 103–117 (2003)
Motion Picture Association of America, I., Theatrical Market Statistics Report, Motion Picture Association of America, Inc. p. 31 (2013)
Motion Picture Association of America, I., MPAA Economic Review (2004)
Eliashberg, J., Hui, S.K., Zhang, Z.J.: From story line to box office: A new approach for green-lighting movie scripts. Management Science 53(6), 881–893 (2007)
Eliashberg, J., Elberse, A., Leenders, M.A.A.M.: The motion picture industry: Critical issues in practice, current research, and new research directions. Marketing Science 25(6), 638–661 (2006)
Jones, J.M., Ritz, C.J.: Incorporating distribution into new product diffusion models. International Journal of Research in Marketing 8(2), 91–112 (1991)
Krider, R.E., Weinberg, C.B.: Competitive dynamics and the introduction of new products: The motion picture timing game. Journal of Marketing Research 35(1), 1–15 (1998)
Ainslie, A., Drèze, X., Zufryden, F.: Modeling movie life cycles and market share. Marketing Science 24(3), 508–517 (2005)
Zufryden, F.S.: Linking advertising to box office performance of new film releases: A marketing planning model. Journal of Advertising Research 36, 29–42 (1996)
Ravid, S.A.: Information, blockbusters, and stars: A study of the film industry. The Journal of Business 72(4), 463–492 (1999)
Hennig-Thurau, T., Houston, M.B., Sridhar, S.: Can good marketing carry a bad product? Evidence from the motion picture industry. Marketing Letters 17(3), 205–219 (2006)
Litman, B.R., Kohl, L.S.: Predicting financial success of motion pictures: The ’80s experience. Journal of Media Economics 2(2), 35–50 (1989)
Simonton, D.K.: Cinematic success criteria and their predictors: The art and business of the film industry. Psychology & Marketing 26(5), 400–420 (2009)
Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications 30(2), 243–254 (2006)
Malczewski, J.: On the use of weighted linear combination method in GIS: Common and best practice approaches. Transactions in GIS 4(1), 5–22 (2000)
Dawes, R.M.: The robust beauty of improper linear models in decision making. American Psychologist 34(7), 571–582 (1979)
Johnson, J.W., LeBreton, J.M.: History and use of relative importance indices in organizational research. Organizational Research Methods 7(3), 238–257 (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Lee, F.L.F.: Cultural discount and cross-culture predictability: Examining the box office performance of American movies in Hong Kong. Journal of Media Economics 19(4), 259–278 (2006)
Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. IEEE Press, Piscataway (2003)
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Hsu, PY., Shen, YH., Xie, XA. (2014). Predicting Movies User Ratings with Imdb Attributes. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_41
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DOI: https://doi.org/10.1007/978-3-319-11740-9_41
Publisher Name: Springer, Cham
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