A point of interest recommendation model based on tracks and friend relationship of users
Point of interest(POI)recommendation is one of the most important applications of location-based social networking(LBSN).Existing studies have proposed methods that utilize POI in-formation and spatio-temporal information for recommendation,but the relevant auxiliary information has not been fully utilized,so it can not alleviate the problems of insufficient information and less travel data caused by short track check-in of users.To solve these problems,a friends relationship and self-attention recommendation model ATFR is proposed.The prediction model consists of three parts:Firstly,the representation vector of friend relationship is obtained by graph embedding method and input into the neural network.Secondly,the user interest preference vector is obtained by GRU.Then,the self-attention mechanism is used to model the sequential and social effects of the user check-in sequence and selectively focus on the historical check-in records associated with the check-in sequence.Finally,the future interest points are recommended according to the interest points sorting list.Experi-mental results on two public datasets show that this model performs better than other algorithms and can be used to improve the quality of personalized interest recommendation services for websites and applications.
point of interest recommendationfriends relationshipsocial networkself-attention mechanism
万方数据
维普
