An effective location-based information filtering system on mobile devices

As mobile devices evolve, research on providing location-based services attract researchers interest. A location-based service recommends information based on users geographical location provided by a mobile device. Mobile devices are engaged with users daily activities and lots of information and services are requested by users, so suggesting the proper information on mobile devices that reflects user preferences becomes more and more difficult. Lots of recent studies have tried to tackle this issue but most of them are not successful because of reasons such as using large datasets or making suggestions based on dynamically collected ratings within different groups instead of focusing on individuals. In this paper, we propose a location based information filtering system that exposes users preferences using Bayesian inferences. A Bayesian network is constructed with conditional probability table while Users characteristics and location data are gathered by using the mobile device. After preprocessing those data, the system integrates that information and uses time to produce the most accurate suggestions. We collected a dataset from 20 restaurants in Malaysia and we gathered behavioral data from two registered users for 7 days. We conducted experiment on the dataset to demonstrate effectiveness of the proposed system and to explain user preferences.

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