社会网络环境下基于信任传递的推荐模型研究

现有基于信任的推荐算法中没有充分挖掘用户间的信任关系，且缺乏合理的信任关系传递规则，极大地影响了推荐算法的可靠性和准确性。针对上述问题，通过用户评分数据与用户的社会关系建立信任传递模型，提出一种基于信任传递的推荐算法。该算法首先利用评分数据计算信任传递模型中用户的隐式直接信任关系，其次通过求解有序加权平均算子融合多条信任传递链的间接信任关系，最后将计算出的用户信任度与相似度融合为综合相似度进行预测推荐。实验结果证实了所提算法可有效提升系统的推荐质量。

Research on recommendation model based on trust transitivity in social network environment

The current trust-based recommendation algorithms did not fully exploit the trust relationship between users and it lacked reasonable trust transitivity rules, which greatly affected the reliability and accuracy of the recommendation algorithm. Aiming at the above problems, this paper combined user rating data with the user''s social relationship to build a trust transitivity model, and proposed a recommendation algorithm based on trust transitivity. Firstly, the algorithm used the score data to calculate the implicit direct trust relationship of the users in the trust transitivity model. Secondly, it solved the indirect trust relationship of multiple trust transitivity chains by solving the ordered weighted average operator. Finally, it converge the user''s trust and similarity into comprehensive similarity for predictive recommendation. The experimental results show that the proposed algorithm can effectively improve the recommendation quality of the system.