Abstract
In the recent past, machine learning algorithms have been used effectively to identify interesting patterns from volumes of data, and aid the decision making process in business environments. In this paper, we aim to use the power of such algorithms to predict the pre-release box-office success of motion pictures. The problem of forecasting the box-office collection for a movie is reduced to the problem of classifying the movie into one of several categories based on its revenue. We propose a novel approach to constructing and using a graph network between movies, thus alleviating the movie independence assumption that traditional learning algorithms make. Specifically, the movie network is first used with a transductive algorithm to construct features for classification. Subsequently, a classifier is learned and used to classify new movies with respect to their predicted box-office collection. Experimental results show that the proposed approach improves the classification accuracy as compared to a fully independent setting.
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Parimi, R., Caragea, D. (2013). Pre-release Box-Office Success Prediction for Motion Pictures. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_44
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DOI: https://doi.org/10.1007/978-3-642-39712-7_44
Publisher Name: Springer, Berlin, Heidelberg
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