基于社交信任和标签偏好的景点推荐方法

针对现有的景点推荐算法在处理用户关系时忽视了用户隐性信任和信任传递问题，以及当用户处于新城市时由于缺乏用户历史记录无法做出准确推荐的情况，本文提出一种综合用户信任关系和标签偏好的个性化景点推荐方法.在仅仅考虑用户相似度时推荐质量差的情况下引入信任度，通过挖掘用户隐性信任关系解决了现有研究在直接信任难以获取时无法做出推荐的情况，有效缓解了数据稀疏性和冷启动问题.同时在用户兴趣分析过程中将景点和标签的关系扩展到了用户、景点和标签三者的相互关系，把用户的兴趣偏好分解成对不同景点标签的长期偏好，有效地缓解了缺乏用户历史游览记录时推荐质量不佳的问题.通过在Flickr网站上收集的数据进行实验验证，结果表明本文提出的混合推荐算法有效地提高了推荐精度，在一定程度上缓解了冷启动和新城市问题.

Attraction Recommendation Algorithm Based on Social Trust and Tag Preferences

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.