Abstract
For the limitation that Chinese movie box office forecasting accuracy is not high in the long-term prediction research, based on the research of the Chinese movie market, this paper proposes a long-term prediction model for movie box office based on the deep belief network. The new model improved the movie box office influence model of Barry, screened out the effective box office impact factor, normalized the quantitative factor and formed a measurement system which is suitable for the Chinese movie market. Based on this measurement system, the characteristics of the data set in the original space are transferred to the space with semantic features and a hierarchical feature representation by deep learning, thus the accuracy of box office prediction was improved. Experimental evaluation results show that, in view of the 439 movie data, the DBN prediction model of movie box office has better prediction performance, and has good application value in the field of film box office.
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Wang, W., Xiu, J., Yang, Z., Liu, C. (2018). A Deep Learning Model for Predicting Movie Box Office Based on Deep Belief Network. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_51
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DOI: https://doi.org/10.1007/978-3-319-93818-9_51
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