Rating prediction via generative convolutional neural networks based regression

Ning, X; Yac, L; Wang, X; Benatallah, B; Dong, M; Zhang, S

Rating prediction via generative convolutional neural networks based regression

Ning, X Yac, L Wang, X

Benatallah, B Dong, M Zhang, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2020, 132, pp. 12-20
Issue Date:: 2020-04

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Field	Value	Language
dc.contributor.author	Ning, X
dc.contributor.author	Yac, L
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Benatallah, B
dc.contributor.author	Dong, M
dc.contributor.author	Zhang, S
dc.date.accessioned	2021-04-10T01:44:32Z
dc.date.available	2021-04-10T01:44:32Z
dc.date.issued	2020-04
dc.identifier.citation	Pattern Recognition Letters, 2020, 132, pp. 12-20
dc.identifier.issn	0167-8655
dc.identifier.issn	1872-7344
dc.identifier.uri	http://hdl.handle.net/10453/147981
dc.description.abstract	Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies’ intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.
dc.language	English
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DE180100251
dc.relation.ispartof	Pattern Recognition Letters
dc.relation.isbasedon	10.1016/j.patrec.2018.07.028
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Rating prediction via generative convolutional neural networks based regression
dc.type	Journal Article
utslib.citation.volume	132
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-04-10T01:44:30Z
pubs.publication-status	Published
pubs.volume	132

Abstract:

Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies’ intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.

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http://hdl.handle.net/10453/147981