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With the rapid development of location-based social networks (LBSNs), spatialitem recommendation has become an important means to help people discoverattractive and interesting venues and events, especially when users travel outof town. However, this recommendation is very challenging compared to thetraditional recommender systems. A user can visit only a limited number ofspatial items, leading to a very sparse user-item matrix. Most of the itemsvisited 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 awayplace. Moreover, user interests and behavior patterns may vary dramaticallyacross different geographical regions. In light of this, we propose Geo-SAGE, ageographical sparse additive generative model for spatial item recommendationin this paper. Geo-SAGE considers both user personal interests and thepreference of the crowd in the target region, by exploiting both theco-occurrence pattern of spatial items and the content of spatial items. Tofurther alleviate the data sparsity issue, Geo-SAGE exploits the geographicalcorrelation by smoothing the crowd's preferences over a well-designed spatialindex structure called spatial pyramid. We conduct extensive experiments toevaluate the performance of our Geo-SAGE model on two real large-scaledatasets. The experimental results clearly demonstrate our Geo-SAGE modeloutperforms the state-of-the-art in the two tasks of both out-of-town andhome-town recommendations.

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