基于加权K-Means和局部BPNN的票房预测模型
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摘要
电影作为典型的短周期、体验型产品,其票房收益受众多因素的共同影响,因此对其票房进行预测较为困难.本文主要构建了一种基于加权K-均值以及局部BP神经网络(BPNN)的票房预测模型对目前的票房预测模型存在的不足进行改进,从而提高票房预测的精度:(1)构建基于随机森林的影响因素影响力测量模型,并以此为依据对票房影响因素进行筛选,以此来简化后续预测模型的输入;(2)考虑到不同影响因素对票房的影响力不同的现实情况,为了解决以往研究中对影响因素权重平均分配的问题,本文构建了基于加权K-均值和局部BP神经网络的票房预测模型,以因素影响力为依据对样本数据进行加权的K-均值聚类,并基于子样本构建局部BP神经网络模型进行票房预测.实验证明,本文所构建的模型平均绝对百分比误差(MAPE)为8.49%,低于对比实验的10.39%,可以看出本文构建的基于加权K-均值以及局部BP神经网络的票房预测模型的预测结果要优于对比模型的预测结果.
As a typical short cycle and experiential product, Movie's box-office is influenced by many factors, so it is hard to forecast its box-office accurately. In this study, a box-office forecasting model based on weighted K-means and local BP Neural Network(BPNN) is constructed, with aims to improve the shortcomings of the current model and improve the accuracy of box office prediction:(1) Construct the factor influence measurement model based on Random Forest(RF)and simplify the box-office influence factors according to the value of variable importance, to achieve the purpose of simplifying the input of the following forecasting model.(2) In the traditional researches, the weight of each factor was equally allocated in sample classification, which without considering the question of different factor has different influence. So a box-office forecasting model based on weighted K-means and local BPNN is constructed, using weighted K-means clustering to classify the samples based on the value of factor influence, then build several local BPNN models based on each subsample. Experiments show that the Mean Absolute Percentage Error(MAPE) of this study's model is8.49%, which is lower than 10.39% of the contrast experiment, which proves the superiority of the box-office forecasting model built in this study.
As a typical short cycle and experiential product, Movie's box-office is influenced by many factors, so it is hard to forecast its box-office accurately. In this study, a box-office forecasting model based on weighted K-means and local BP Neural Network(BPNN) is constructed, with aims to improve the shortcomings of the current model and improve the accuracy of box office prediction:(1) Construct the factor influence measurement model based on Random Forest(RF)and simplify the box-office influence factors according to the value of variable importance, to achieve the purpose of simplifying the input of the following forecasting model.(2) In the traditional researches, the weight of each factor was equally allocated in sample classification, which without considering the question of different factor has different influence. So a box-office forecasting model based on weighted K-means and local BPNN is constructed, using weighted K-means clustering to classify the samples based on the value of factor influence, then build several local BPNN models based on each subsample. Experiments show that the Mean Absolute Percentage Error(MAPE) of this study's model is8.49%, which is lower than 10.39% of the contrast experiment, which proves the superiority of the box-office forecasting model built in this study.
引文
1聂鸿迪.中国电影票房的影响因素及其实证研究[硕士学位论文].北京:北京交通大学,2015.
2罗晓芃,齐佳音,田春华.电影首映日后票房预测模型研究.统计与信息论坛,2016,31(11):94-102.[doi:10.3969/j issn.1007-3116.2016.11.016]
3郑坚,周尚波.基于神经网络的电影票房预测建模.计算机应用,2014,34(3):742-748.
4韩忠明,原碧鸿,陈炎,等.一个有效的基于GBRT的早期电影票房预测模型.计算机应用研究,2018,35(2):410-416.[doi:10.3969/j.issn.1001-3695.2018.02.020]
5刘涛.面向社交媒体的电影票房预测技术的研究与应用[硕士学位论文].石家庄:河北科技大学,2016.
6王炼,贾建民.基于网络搜索的票房预测模型--来自中国电影市场的证据.系统工程理论与实践,2014,34(12):3079-3090.[doi:10.12011/1000-6788(2014)12-3079]
7郝媛媛,邹鹏,李一军,等.基于电影面板数据的在线评论情感倾向对销售收入影响的实证研究.管理评论,200921(10):95-103.
8丘萍,张鹏.第三方网络口碑对短生命周期产品销量的影响研究.河海大学学报(哲学社会科学版),2017,19(2):39-46.
9 Lee JH,Jung SH,Park JH.The role of entropy of review text sentiments on online WOM and movie box office sales Electronic Commerce Research and Applications,2017,22:42-52.[doi:10.1016/j.elerap.2017.03.001]
10袁海霞.网络口碑的跨平台分布与在线销售--基于BP人工神经网络的信息熵与网络意见领袖敏感性分析.经济管理,2015,37(10):86-95.[doi:10.3969/j.issn.1007-5097.2015.10.013]
11 Du JF,Xu H,Huang XQ.Box office prediction based on microblog.Expert Systems with Applications,2014,41(4):1680-1689.[doi:10.1016/j.eswa.2013.08.065]
12 Hur M,Kang P,Cho S.Box-office forecasting based on sentiments of movie reviews and independent subspace method.Information Sciences,2016,372:608-624.[doi:10.1016/j.ins.2016.08.027]
13 Kim T,Hong J,Kang P.Box office forecasting using machine learning algorithms based on SNS data International Journal of Forecasting,2015,31(2):364-390[doi:10.1016/j.ijforecast.2014.05.006]
14魏明强,黄媛.网络评价对电影票房走势的影响.中国传媒大学学报自然科学版,2017,24(3):68-71.
15 Zhang L,Luo JH,Yang SY.Forecasting box office revenue of movies with BP neural network.Expert Systems with Applications,2009,36(3):6580-6587.[doi:10.1016/j.eswa2008.07.064]
16李金芝.基于泛函网络的票房预测研究与应用[硕士学位论文].重庆:重庆大学,2015.
17姚登举.面向医学数据的随机森林特征选择及分类方法研究[博士学位论文].哈尔滨:哈尔滨工程大学,2016.
18曹正凤.随机森林算法优化研究[博士学位论文].北京:首都经济贸易大学,2014.
19陈小雪,尉永清,任敏,等.基于萤火虫优化的加权K-means算法.计算机应用研究,2018,35(2):466-470.[doi:10.3969/j.issn.1001-3695.2018.02.031]
2罗晓芃,齐佳音,田春华.电影首映日后票房预测模型研究.统计与信息论坛,2016,31(11):94-102.[doi:10.3969/j issn.1007-3116.2016.11.016]
3郑坚,周尚波.基于神经网络的电影票房预测建模.计算机应用,2014,34(3):742-748.
4韩忠明,原碧鸿,陈炎,等.一个有效的基于GBRT的早期电影票房预测模型.计算机应用研究,2018,35(2):410-416.[doi:10.3969/j.issn.1001-3695.2018.02.020]
5刘涛.面向社交媒体的电影票房预测技术的研究与应用[硕士学位论文].石家庄:河北科技大学,2016.
6王炼,贾建民.基于网络搜索的票房预测模型--来自中国电影市场的证据.系统工程理论与实践,2014,34(12):3079-3090.[doi:10.12011/1000-6788(2014)12-3079]
7郝媛媛,邹鹏,李一军,等.基于电影面板数据的在线评论情感倾向对销售收入影响的实证研究.管理评论,200921(10):95-103.
8丘萍,张鹏.第三方网络口碑对短生命周期产品销量的影响研究.河海大学学报(哲学社会科学版),2017,19(2):39-46.
9 Lee JH,Jung SH,Park JH.The role of entropy of review text sentiments on online WOM and movie box office sales Electronic Commerce Research and Applications,2017,22:42-52.[doi:10.1016/j.elerap.2017.03.001]
10袁海霞.网络口碑的跨平台分布与在线销售--基于BP人工神经网络的信息熵与网络意见领袖敏感性分析.经济管理,2015,37(10):86-95.[doi:10.3969/j.issn.1007-5097.2015.10.013]
11 Du JF,Xu H,Huang XQ.Box office prediction based on microblog.Expert Systems with Applications,2014,41(4):1680-1689.[doi:10.1016/j.eswa.2013.08.065]
12 Hur M,Kang P,Cho S.Box-office forecasting based on sentiments of movie reviews and independent subspace method.Information Sciences,2016,372:608-624.[doi:10.1016/j.ins.2016.08.027]
13 Kim T,Hong J,Kang P.Box office forecasting using machine learning algorithms based on SNS data International Journal of Forecasting,2015,31(2):364-390[doi:10.1016/j.ijforecast.2014.05.006]
14魏明强,黄媛.网络评价对电影票房走势的影响.中国传媒大学学报自然科学版,2017,24(3):68-71.
15 Zhang L,Luo JH,Yang SY.Forecasting box office revenue of movies with BP neural network.Expert Systems with Applications,2009,36(3):6580-6587.[doi:10.1016/j.eswa2008.07.064]
16李金芝.基于泛函网络的票房预测研究与应用[硕士学位论文].重庆:重庆大学,2015.
17姚登举.面向医学数据的随机森林特征选择及分类方法研究[博士学位论文].哈尔滨:哈尔滨工程大学,2016.
18曹正凤.随机森林算法优化研究[博士学位论文].北京:首都经济贸易大学,2014.
19陈小雪,尉永清,任敏,等.基于萤火虫优化的加权K-means算法.计算机应用研究,2018,35(2):466-470.[doi:10.3969/j.issn.1001-3695.2018.02.031]