PRME-GTS: A New Successive POI Recommendation Model with Temporal and Social Influences

Abstract

Successive point-of-interest (POI) recommendation is an important research task which can recommend new POIs the user has not visited before. However, the existing researches for new successive POI recommendation ignore the integration of time information and social relations information which can improve the prediction of the system. In order to solve this problem, we propose a new recommendation model called PRME-GTS that incorporates social relations and temporal information in this paper. It can models the relations between users, temporal information, points of interest, and social information, which is based on the framework of pair-wise ranking metric embedding. Experimental results on the two datasets demonstrate that employing temporal information and social relations information can effectively improve the performance of the successive point-of-interest (POI) recommendation.