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A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

文献类型：期刊论文

作者Zhang, Xun1; Xie, Xiaolan1; Wang, Yuxue1; Zhang, Xiaohu2; Jiang, Dong3; Yu, Chongchong1; Liang, Yike1
刊名IEEE ACCESS
出版日期2020
卷号8页码:71708-71720
关键词Classification algorithms Neural networks Prediction algorithms Roads Data models Backpropagation Advertising Digital signage backpropagation algorithms classification algorithms geographic information systems
ISSN号2169-3536
DOI10.1109/ACCESS.2020.2987717
通讯作者Jiang, Dong(jiangd@igsnrr.ac.cn) ; Yu, Chongchong(chongzhy@vip.sina.com)
英文摘要Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into ,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multi-label classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at various scales. Finally, four metrics are used to examine the effectiveness of the proposed model. The results show that the Huff-BP-based multi-label classification model achieves relatively good classification results, and the digital signage audiences are mainly concentrated within the 4th Ring Road and near the 5th Ring Road.
WOS关键词MULTI-LABEL CLASSIFICATION ; AREAL UNIT PROBLEM
资助项目Strategic Priority Research Program of the Chinese Academy of Science[XDA23000000] ; China Postdoctoral Science Foundation[2017M620885] ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of the 13th Five-year Plan[CITTCD201904037] ; Research and Development Programme of the Beijing Municipal Education Commission[KM202010011011] ; National Natural Science Foundation of China[61802010]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000530813500004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Strategic Priority Research Program of the Chinese Academy of Science ; China Postdoctoral Science Foundation ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of the 13th Five-year Plan ; Research and Development Programme of the Beijing Municipal Education Commission ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/159670]  
专题中国科学院地理科学与资源研究所
通讯作者Jiang, Dong; Yu, Chongchong
作者单位1.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China
2.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res IGSNRR, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xun,Xie, Xiaolan,Wang, Yuxue,et al. A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network[J]. IEEE ACCESS,2020,8:71708-71720.
APA Zhang, Xun.,Xie, Xiaolan.,Wang, Yuxue.,Zhang, Xiaohu.,Jiang, Dong.,...&Liang, Yike.(2020).A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network.IEEE ACCESS,8,71708-71720.
MLA Zhang, Xun,et al."A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network".IEEE ACCESS 8(2020):71708-71720.

入库方式： OAI收割

来源：地理科学与资源研究所

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