Location recommendation algorithm for online social networks based on location trust

Recently, with the development of mobile location technology and the increasing popularity of social networks, location recommendation technology based on location-based social network(LBSN) has attracted more and more attention. The previous location recommendation algorithms are realized by spatial clustering based on geographical location and the longest public access sequence of users. There are low recommended accuracy rate for the previous recommended methods without considering the users access recommended location probability events. In view of the problems, the TBLR algorithm is proposed by using the premise of considering the users' similar location preference, users' trust and the probability of users access location.etc. This algorithm effectively bridges the gap between the offline behavior of users in the real world and online social network information in the virtual community. In addition, genetic algorithm is used to optimize the parameters in the algorithm. Finally, the result of experiments are shown by using users' check-in data which come from a real LBSN website (Gowalla).