A Point-of-interest Recommendation Method Based on Hawkes Process

Point-of-interest (POI) recommendation is a crucial personalized location service in LBSNs.To cope with the complexity and extreme sparsity of users check-in data,we proposed a context-aware collaborative filtering POI recommendation algorithm based on Hawkes process (HWCF).First,we analyzed users' behavior characteristics according to the geographic spatial clustering phenomenon of users' check-in POI,and filtered users' candidate POI.Then,we utilized Hawkes process to model candidate POI.Integrated different context information,such as spatial distance,spatial sequence transformation,temporal,users' preferences,POI popularity,etc.to compute the visiting probability of candidate POI for every user,and then obtained the top-k recommendation list by sorting the visiting probability.Finally,we discussed the range and adjustment of parameters in HWCF algorithm.Experimental results show that HWCF achieves better performance compared to other advanced POI recommendation algorithms