Mobile Location Recommendation
The widespread use of mobile communication devices has generated a large amount of interest in location-based services, particularly, those services that are based on information about locations near to users of those devices. Examples are: giving directions or advice about different routes and recommending nearby landmarks to users. The location of a user provides context to a recommendation, and a user model, built over time from the user's navigation traces, provides behavioural patterns which inform on the type of recommendation to make.
In this research work, we are looking into mobile location recommendation from a collective model of users movements. The collective model is built from an aggregate of many trails from multiple users, and thus does not compromise the privacy of any individual. Our research shows that an aggregate model can give accurate predictions, despite the loss of information about individual users. Moreover, the aggregate model has potential in providing social recommendations based on other users preferences.