NDLI: Modelling on movie box-office prediction based on LFM algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ruan, Dong-ru Liu, Tao Gao, Kai
Copyright Year	2015
Description	Author affiliation: School of Information Science & Engineering, Hebei University of Science and Technology, China, 050000 (Ruan, Dong-ru; Liu, Tao; Gao, Kai)
Abstract	Concerning the limitations that the accuracy of predication is lower in the traditional model of movie box-office prediction and classification, this paper proposes a novel model of movie box-office revenue prediction. The model is based on the relationship of movie box-office and the user behaviors. The algorithm could be summarized as follows. Firstly, this paper uses the Latent Factor Model (LFM) to classify the movies. Secondly, for different categories, this paper constructs a series of linear movie box-office prediction models. And these models use total box-office as the dependent variable, while the independent variables are user reviews and user ratings which are the major manifestation in vertical media. Finally, this paper uses the experimental results to adjust the independent variables in different models. The experimental results demonstrate that the model performs better on prediction than the traditional methods.
Starting Page	1
Ending Page	5
File Size	318366
Page Count	5
File Format	PDF
ISBN	9780956715753
DOI	10.1109/ICMIC.2015.7409369
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-18
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	University of Al Qayrawan, Tunisia
Subject Keyword	LFM Linear regression Linear prediction models Production Predictive models Media Motion pictures Prediction algorithms Vertical media Box-office Classification algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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