Understanding customer behaviour in urban shopping mall from WiFi logs

Yuanyi Chen, Jinyu Zhang, Minyi Guo, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Traditional ways of understanding customer behaviour are mainly based on predominantly field surveys, which are not effective as they require labor-intensive survey. As mobile devices and ubiquitous sensing technologies are becoming more and more pervasive, user-generated data from these platforms are providing rich information to uncover customer preference. In this study, we propose a shop recommendation model for urban shopping mall by exploiting user-generated WiFi logs to learn customer preference. Specifically, the proposed model consists of two phases: 1) offline learning customer's preference from their check-in activities; 2) online recommendation by fusing the learnt preference and temporal influence. We have performed a comprehensive experiment evaluation on a real dataset collected by over 39,000 customers during 7 months, and the experiment results show the proposed recommendation model outperforms state-of-the-art methods.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
PublisherIEEE
Pages50-53
Number of pages4
ISBN (Electronic)9781509043385
DOIs
Publication statusPublished - 2 May 2017
Event2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 - Kona, Big Island, United States
Duration: 13 Mar 201717 Mar 2017

Conference

Conference2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
Country/TerritoryUnited States
CityKona, Big Island
Period13/03/1717/03/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Cite this