LARS*: An Efficient and Scalable Location-Aware Recommender System

Computer Engineering

The problem of hyper-local place ranking. Given a user location and query string (e.g., “Indian restaurant"), hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries (e.g., map direction searches from point A to point B).This paper proposes LARS*, a location-aware recommender system that uses their location-based ratings to show recommendations. Traditional recommender systems do not have spatial properties of users nor items; LARS*, next, 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 Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches. Our proposed location-aware recommender system, tackles a problem untouched by traditional recommender systems by dealing with three types of location-based ratings: spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* employs user partitioning and travel penalty techniques to support spatial ratings and spatial items, respectively.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.