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Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more interesting unknown locations and facilitate service providers to provide users with more accurate notifications or advertisements. Some existing work has addressed the data sparsity problem of collaborative filtering by incorporating contextual information into the model. However, they ignore the sequence relationship contained in the user’s historical check-in records, which makes it difficult to accurately model the user’s preference and affects the final recommendation results. To acquire users’ preference for a location more accurately, this paper proposes a new POI recommendation framework exploiting sequential, category, and geographical influence. Firstly, we obtain the latent vector of POI and the latent vector of the user’s preference for POI from the user’s check-in sequence based on the word embedding model. Next, a virtual common access sequence for users is constructed according to the user’s check-ins, a new similarity computation method is present combining category differentiation and POI latent vector. Then, we apply it to the collaborative filtering framework to get the user’s behavioral preference probability of POI. In addition, the kernel density estimation method is employed to get the user’s geographical preference probability of POI by considering the geographical influence. Finally, the POI recommendation list is obtained by the weighted fusion of the two users’ preference probability to improve the performance of the POI recommendation. Experimental results on two datasets indicate that the proposed method has better performance in terms of three evaluation metrics than the other five POI recommendation methods.

A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence