Location Recommendation of Digital Signage Based on Multi-Source Information Fusion

Xiaolan Xie, Xun Zhang, Jingying Fu, Dong Jiang, Chongchong Yu and Min Jin
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Xiaolan Xie: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Xun Zhang: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Jingying Fu: Key Laboratory of Resources Utilization and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Dong Jiang: Key Laboratory of Resources Utilization and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Chongchong Yu: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Min Jin: Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Sustainability, 2018, vol. 10, issue 7, 1-21

Abstract: With the increasing amount of digital signage and the complexity of digital signage services, the problem of introducing precise location recommendation methods for digital signage should be solved by digital signage enterprises. This research aims to provide a sustainable location recommendation model that integrates the spatial characteristics of geographic locations and multi-source feature data to recommend locations for digital signage. We used the outdoor commercial digital signage within the Sixth Ring Road area in Beijing as an example and combined it with economic census, population census, average house prices, social network check-in data, and the centrality of traffic networks that have an impact on the sustainable development of the regional economy as research data. The result shows that the proposed method has higher precision and recall in location recommendation, which indicates that this method has a better recommendation effect. It can further improve the recommendation quality and the deployment of digital signage. By this method, we can optimize resource allocation and make the economics and resources sustainable. The digital signage recommendation results of the Beijing City Sixth Ring Road indicated that the areas suitable for digital signage were primarily distributed in Wangfujing, Financial Street, Beijing West Railway Station, and tourist attractions in the northwest direction of the Fifth Ring Road. The research of this paper not only provides a reference for the integration of geographical features and their related elements data in a location recommendation algorithm but also effectively improves the science of digital signage layout, prompting advertising efforts to advance precision, personalization, low carbonization, and sustainable development.

Keywords: location recommendation; digital signage; spatial features; multi-source information; region division (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
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