Probabilistic Category-based Location Recommendation Utilizing Temporal Influence and Geographical Influence

Location recommendation provides unvisited locations to the users for the rapidly growing location-based social networks. The service is based on the users' visiting histories and location related information such as location categories. In this paper, we propose a location recommendation algorithm called sPCLR that recommends locations to the users at a given time of the day by utilizing category information. The algorithm considers both temporal and spatial components. The temporal component utilizes the temporal influence of similar users' check-in behaviors by representing a user's periodic check-in behavior at different location categories as temporal curves. The similarity between users' periodic check-in behavior is calculated based on the difference between temporal curves. The spatial component utilizes the geographical influence of locations and filters out those locations that are not of interest to the user. The performance of sPCLR is compared with three existing location recommendation algorithms on a real-world dataset. Experimental results show that the sPCLR algorithm performs better than all other three algorithms.