Point-of-Interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geosusceptibility of POI, and their physical distance. Geo-influence captures POI's capacity at exerting geographical influence to other POIs, and geosusceptibility reflects POI's propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.
For example, in order to improve the performance of POI recommendation, Wang et al. REF integrated the geographical influence of POIs into a standard recommendation model to capture a user's preference.
Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation
IJCAI
Computer Science