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 language | English |
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Title of host publication | 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 |
Publisher | IEEE |
Pages | 50-53 |
Number of pages | 4 |
ISBN (Electronic) | 9781509043385 |
DOIs | |
Publication status | Published - 2 May 2017 |
Event | 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 - Kona, Big Island, United States Duration: 13 Mar 2017 → 17 Mar 2017 |
Conference
Conference | 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 |
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Country/Territory | United States |
City | Kona, Big Island |
Period | 13/03/17 → 17/03/17 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications