Exploring multiple spatio-temporal information for point-of-interest recommendation

Traditional collaborative filtering methods perform poorly in providing location recommendations due to the high sparsity of users' check-in data, prompting the development of new location recommendation approaches that can integrate situational factors such as time and location. Using long short-term memory (LSTM) neural networks and kernel density estimation (KDE), this paper integrates the impact of point-of-interest (POI) location and category on users' check-in behavior according to check-in sequence data. First, LSTM neural networks are used to model users' periodic and repetitive daily activities for a sequence-based prediction of the probability of whether the user will visit a candidate POI. Second, the user's geographical preference in the two-dimensional space is represented by KDE and used to make a location-based check-in probability prediction. Next, the user's category preference is used to predict the check-in probability of a candidate POI. Finally, a user preference model is constructed from three perspectives of time, location, and category, and the comprehensive check-in probability is used for Top-N recommendation. The validation experiments on Foursquare dataset verifies that, in terms of recommendation precision and recall, the proposed recommendation method is superior to both the basic LSTM approach and the method that uses only location information. In addition, it is experimentally confirmed that the geographical preference, which is reflected by clustering of a user's check-in locations, is stable, but the user's category preference is prone to drift.