Abstract: In location-based social networks (LBSN),users share their location and content related to location information.Point-of-interest (POI) recommendation is an important application in LBSN which recommends locations that might be of interest to users.However,compared with other recommendation problems (such as product and movie recommendation),the users' prefe-rence for POI is particularly determined by the time feature.In this paper,the influence of time feature on POI recommendation task is explored,and a time-aware POI recommendation method is proposed,called TAPR (Time Aware POI Recommendation).Our method constructs different relation matrices based on different time scales,and uses tensor decomposition to decompose the constructed multiple relation matrices to obtain the representation of the user and the POI.Finally,our method uses cosine similarity to calculate similarity scores between users and non-visited POIs,and combines the algorithm of user preference modeling to obtain the final recommendation score.Experimental results on two public datasets show that the proposed TAPR performs better than other POI recommendation methods.

Key words: POI recommendation, Representation learning, Tensor decomposition, Time aware feature