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Mobile Information Systems
Volume 2017, Article ID 9601404, 16 pages
Research Article

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

1Hangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, China
2Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Correspondence should be addressed to Yuanyi Chen; nc.ude.utjs@zxyyc

Received 11 March 2017; Revised 28 May 2017; Accepted 14 June 2017; Published 31 July 2017

Academic Editor: Michele Ruta

Copyright © 2017 Zengwei Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Indoor shopping trajectories provide us with a new approach to understanding user’s behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a location-aware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user’s shopping experience but also help the store owner better understand user’s shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top- recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.