Abstract
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-k 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.
Highlights
Indoor location-based services, such as shopping flow monitoring, mobile location-based advertisement, and POI recommendation, are expected to witness a significant growth in the decade due to the popularity of mobile devices and the development of indoor positioning technologies
The reason is that the convergence of random walk with restart is determined by the parameter λ; that is, a greater λ leads to faster convergence and can make better recommendation
The performance improvement for group C users is about 13% and 8% compared with user-based CF and random walk with restart (RWR); (2) the performance improvement of recommendation model using random walk is significant for “cold-start” users
Summary
Indoor location-based services, such as shopping flow monitoring, mobile location-based advertisement, and POI recommendation, are expected to witness a significant growth in the decade due to the popularity of mobile devices and the development of indoor positioning technologies. Previous studies about this topic mainly focus on providing some basic services, such as indoor positioning [1], indoor navigation [2], or indoor tracking [3]. Few studies aim to perform in-depth analysis and utilize user’s location information in indoor environment, which is a fundamental context for location-based services. WiFi check-in logs provide a new platform to generate user’s trajectory in indoor environment since free WiFi is increasing available for many indoor spaces, such as urban shopping mall and Mobile Information Systems
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