Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LB-SNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recom-mendation is very challenging compared to the traditional recom-mender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and be-havior patterns may vary dramatically across different geographi-cal regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE ex-ploits the geographical correlation by smoothing the crowd’s pref-erences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments and the experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art