STPR: A Personalized Next Point-of-Interest Recommendation Model with Spatio-Temporal Effects Based on Purpose Ranking

Abstract

The problem of personalized next point-of-interest (POI) recommendation is significant and of practical value in location-based social networks (LBSNs). Due to the sparsity of data in regard to check-ins, POI recommendations remain a challenging problem. Previous work developed recommendation models by taking into consideration the properties of user’s mobility, for example, spatio-temporal information and the similarity of movement rules. However, they ignore the influence of trip-purpose on user’s mobility, and the memory effect of historical check-in behavior at the same location. To cope with this problem, a POI recommendation model with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> patio- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> emporal effects based on <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> urpose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> anking (STPR) is proposed. STPR contains two important phases: (1) Purpose prediction: classifying the POIs in the spatio-temporal database into four categories. Each category corresponds to a purpose. Then, a purpose ranking model is constructed to model the selection of user’s intended purpose for the trip. (2) The scoring of each candidate POI: the properties of spatial and temporal information are taken into consideration when calculating the score. For the spatial property, the kernel density estimation is used to estimate the visiting probability of a certain POI. For the temporal property, we focus on the time interval and the POI exploration mechanism. We find that POI exploration mechanism can reflect a user’s mobility of visiting new POIs. Furthermore, a method based on Bayesian personalized ranking, called BPR, is proposed to estimate the time interval for unvisited POIs. Extensive experiments were conducted on two real datasets, and the experimental results demonstrate that the recommendation accuracy of the STPR model outperforms the state-of-the-art POI recommendation models with good runtime performance.