Context-Aware Recommender Systems

Traditionally recommender systems have been focusing on recommending the most relevant items to users or the most appropriate users to items.  While the traditional recommendation technologies have performed reasonably well in several applications, in many other applications, such as location- and time-based services, including travel recommendations, it may not be sufficient to consider only users and items - it is also important to incorporate contextual information into the recommendation process.  This talk will review various ways of providing the contextual information and incorporating it into recommendation algorithms, present an approach to context-aware recommendations using multidimensional data modeling, and suggest possible research directions in this area of recommender systems.