Modeling Location-Based User Rating Profiles for Personalized Recommendation

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem.National Natural Science Foundation of China [61272155]; 973 program [2014CB340405]; Australian Research Council (ARC) [DP120102829]; ARC Discovery Project [DP140100545]; Chinese National "111" project, "Attracting International Talents in Data Engineering and Knowledge Engineering Research"SCI(E)ARTICLEh.yin1@uq.edu.au; bin.cui@pku.edu.cn; ling.chen@uts.edu.au; zhitinghu@gmail.com; chengqi.zhang@uts.edu.au3,SI