Recently, I have presented a paper at ICDE 2012. The paper title is: "LARS: A Location-Aware Recommender System". The paper is a joint work with Justin Levandoski, Ahmed Eldawy, and Mohamed Mokbel. The main idea of the paper is to promote the spatial locations of both the users and the items, as first class citizens in existing recommender systems. Here is the abstract:
This paper proposes LARS, a location-aware recommender system that uses location based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
I am really proud of the work in that paper and I wish the findings in that paper are useful in pratice. We will be demonstrating the research ideas presented in the paper in SIGMOD 2012, using a Location-base social networking system (called Sindbad) developed at University of Minnesota.