【摘要】 为克服传统方法在缺少用户个人信息及发布内容的情况下无法有效地评估用户可信度问题,提出基于评论反馈信息和信任关系的用户可信度因子图模型(UFGM)。该模型将信任关系和评论反馈对用户可信度评估的影响形式化为一个概率模型,并提出半监督分类的学习方法构建模型。在Extended Epinions数据集上验证了模型的有效性,并发现信任关系比评论反馈更易对用户可信度评估产生积极影响。与传统算法相比,UFGM能在缺少用户描述信息及评论信息的情况下将用户可信度评估的准确度提高12%～29%。更多还原

【Abstract】 In order to overcome the inability of traditional methods to effectively evaluate user credibility in the absence of user personal information and published content,a user credibility factor graph model(UCFGM)based on comment feedback and trust relationship is proposed, which formalized the influence of trust relationship and comment feedback on user credibility evaluation into a probability model,and proposed a semisupervised classification learning method to build the model. The effectiveness of the model is verified on the Extended Epinions dataset,and it is found that the trust relationship is more likely to have a positive impact on user credibility evaluation than comment feedback.Compared with traditional algorithms, UCFGM can improve the accuracy of user credibility evaluation by 12%to 29% in the absence of user description and comment information.更多还原