A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user's location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of 'context-aware rating', whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.