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

Xun Zhang,Yuxue Wang,Dong Jiang,Chongchong Yu,Yike Liang,Xiaohu Zhang,Xiaolan Xie

doi:10.1109/access.2020.2987717

Abstract

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 100 × 100-1,000 × 1,000 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 multilabel 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.

Highlights

Digital signage refers to a multimedia professional audiovisual system that displays commercial, financial and entertainment information on digital signage terminal display equipment in public places where crowds assemble
We propose an improved classification algorithm of backpropagation (BP) neural network, which solve the problem of multi-label classification, and propose an improved Huff model to calculate the digital signage influence values between each grid cell
To investigate multi-scale classification of digital signage audience data, we introduce multi-label data into the BP neural network classification algorithm to improve it to solve the multilabel classification problem, and the spatial feather of digital signage is integrated into the Huff model to improve it to calculate the digital signage influence values between each grid cell, the improved Huff model is integrated into the improved BP neural network to establish the Huff-BP model

Summary

Introduction

Digital signage refers to a multimedia professional audiovisual system that displays commercial, financial and entertainment information on digital signage terminal display equipment in public places where crowds assemble. Digital signage has become an important channel for offline advertisements in modern cities [1], [2]. With the rapid development of information technology, digital signage has become increasingly popular and been extensively used in public places such as large supermarkets, hotels and airports [6], [7]. As the amount of digital signage grows continuously and the digital signage business becomes increasingly complex, it is important to accurately measure the types of digital signage audiences, which is a vital means for ensuring the normal operation of the digital signage business and, at the same time, can help to improve the accuracy of advertising and allow advertisers to run more favourable advertisements based on.

Methods

Results

Conclusion