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计算机系统应用:2020,29(8):31-37
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基于生成式对抗网络的中国电影连续观影数据预测
(1.上海市崇明区融媒体中心广播电视部, 上海 202158;2.上海大学 上海电影学院规划办公室, 上海 200444)
Continuous Audience Data Prediction of Chinese Films Based on Generative Adversarial Networks
(1.Department of Radio and Television, Chongming Digital Media Center, Shanghai, Shanghai 202158, China;2.Planning Office, Shanghai Film Academy, Shanghai University, Shanghai 200444, China)
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投稿时间:2020-02-10    修订日期:2020-03-08
中文摘要: 近年来, 中国电影上映数量及观影人数均快速上涨, 市场逐步扩大, 各类票房预测算法也被广泛提出和研究. 然而, 这些方法仅利用电影静态信息进行预测, 无法根据上映时期的各类实时动态信息对预测进行调整. 同时, 大部分现存方法仅能预测最终票房, 而无法预测每日票房及其他观影信息, 如上座率, 观影人数等. 为了在上映期间能准确预测电影上映后各日的观影信息, 本文提出一种基于生成式对抗网络(Generative Adversarial Networks, GAN)的预测算法. 该算法首先基于傅里叶变换将指定电影已上映各日票房的时域信号转化为频域信号并提取全局信息. 再利用本文提出的动态编码获取其局部动态信息. 最后通过生成式对抗网络对编码后的输入进行深度频域特征和时域特征提取以及未上映日期观影数据的预测和生成. 实验结果表明, 本文算法可较为精确的预测每日电影观影数据, 同时相对于仅利用静态数据, 本文方法可提升预测准确度, 当两者结合使用时, 可达到最佳效果. 因此, 该算法可为电影营销产业提供有效信息.
Abstract:Recently, the number of Chinese films and audience as well as film market have been increased rapidly, while various box office prediction approaches have been proposed and investigated. However, these approaches only utilized stable information of films for prediction without using any dynamic information, making them unable to adjust predictions in real-time. Meanwhile, current methods only predict total box office of films but ignore everyday’s revenue and other audience information such as attendance rate, the number of audiences. In order to accurately predict each day’s audience data during the screening period, this study proposes a prediction algorithm based on Generative Adversarial Networks (GAN). For a film, this algorithm firstly converts the time-series audience data of available dates to frequency domain using Fourier transform, allowing global feature to be extracted. Then, a novel dynamic encoding method is proposed to obtain the dynamic of recent days. Finally, the deep spectral feature is extracted and future audience data is predicted using GAN. The experimental results show that the proposed algorithm can accurately predict the daily audience data of films. Compared to only using stable information, the proposed dynamic method can improve the prediction accuracy. When combined both stable and dynamic information, the best results are achieved. Therefore, the algorithm can provide useful information for the film marketing industry.
文章编号:7584     中图分类号:    文献标志码:
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引用文本:
姜禄瑶,曲丽萍.基于生成式对抗网络的中国电影连续观影数据预测.计算机系统应用,2020,29(8):31-37
JIANG Lu-Yao,QU Li-Ping.Continuous Audience Data Prediction of Chinese Films Based on Generative Adversarial Networks.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):31-37

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