Location based context aware recommender system through user defined rules

Recommender systems are a subclass of information filtering system and are widely used in the ecommerce domain [13]. They filter huge amount of data to provide personalized recommendations on services or products to users. Most of the existing approaches to develop a recommender system do not take into account contextual information such as weather, day, time, distance and location to provide recommendations. This paper proposes a location based context aware recommender system [9] that uses a ranking function to provide top-k recommendations to the user. The contextual data is defined by the users in the form of rules and RuleML [1] is chosen as a rule based language. When an active user needs recommendations of nearby places then contextual data in the user-defined RuleML rules is extracted, evaluated and top-k recommendations of nearby places based on the ranking function are presented to the user.