基于信任扩展和列表级排序学习的服务推荐方法

Trust expansion and listwise learning-to-rank based service recommendation method

针对传统基于信任网络的服务推荐算法中信任关系稀疏以及通过Qo S预测值排序得到的服务推荐列表不一定最符合用户偏好等问题,提出基于信任扩展和列表级排序学习的服务推荐方法(TELSR)。在分析服务排序位置信息的重要性后给出概率型用户相似度计算方法,进一步提高相似度计算的准确性;利用信任扩展模型解决用户信任关系稀疏性问题,并结合用户相似度给出可信邻居集合构建方法;基于可信邻居集合,利用列表级排序学习方法训练出最优排序模型。仿真实验表明,与已有算法相比,TELSR在具有较高推荐精度的同时,还可有效抵抗恶意用户的攻击。

In view of the problem of trust relationship in traditional trust-based service recommendation algorithm, and the inaccuracy of service recommendation list obtained by sorting the predicted QoS, a trust expansion and listwise learning-to-rank based service recommendation method （TELSR） was proposed. The probabilistic user similarity compu-tation method was proposed after analyzing the importance of service sorting information, in order to further improve the accuracy of similarity computation. The trust expansion model was presented to solve the sparseness of trust relationship, and then the trusted neighbor set construction algorithm was proposed by combining with the user similarity. Based on the trusted neighbor set, the listwise learning-to-rank algorithm was proposed to train an optimal ranking model. Simula-tion experiments show that TELSR not only has high recommendation accuracy, but also can resist attacks from mali-cious users.