In view of that the existing attractions recommendation algorithm ignores the user's implicit trust and trust transfer when dealing with users' relationships, and the difficulties of making accurate recommendation for users in the new city due to the lack of user history records, this paper presents a personalized attraction recommendation algorithm based on users' social trust and tag preferences. According to user's rating behavior and context information, user's implicit trust is tapped, and the trust among users is obtained through trust transfer, which effectively alleviates the data sparsity. Then, by analyzing the relationships among users, attractions, and tags, the user's preference is decomposed into the preference of different attraction labels to further explore the user's long-term interest preferences. Experimental results on the data collected on Flickr website show that the hybrid recommendation algorithm proposed in this study effectively improves the accuracy of recommendation and relieves cold start and new city problems to a certain extent.