Point-of-interest recommendation is one of the key researches in recommendation systems. Traditional algorithms only use user sign-in information for recommendation. They only consider whether users sign in or not, and ignore the frequency of user sign-in and trust relationship. In order to improve the recommendation accuracy, a hybrid recommendation algorithm (UGT) combining user similarity, geographic location and trust relationship is proposed. For sign-in information, sign-in frequency is used to replace the traditional binary sign-in, and the time weight is added to the sign-in information. For user-based collaborative filtering, a neighbor selection strategy is proposed to improve the prediction accuracy. For trust relationship, the user’s attributes are firstly analyzed, then a social status calculation method is given, and a trust degree calculation method is reconstructed. Experiments show that the hybrid algorithm significantly improves accuracy and recall rate in comparison to the traditional recommendation algorithm.

 

location-based social network, point-of-interest recommendation, collaborative filtering, trust relationship, time weight

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