一种基于信任的协同过滤推荐模型

传统的协同过滤推荐技术主要基于用户-项目评价数据集进行挖掘推荐，没有有效地利用用户通信上下文信息，从而制约其进一步提高推荐的精确性。针对传统协同过滤推荐算法存在的推荐精度不高的弊端，在协同过滤算法中融入通信上下文信息，引入了通信信任、相似信任和传递信任三个信任度，并提出了一种基于信任的协同过滤推荐模型。通过公开数据集验证测试，证明提出的推荐算法较传统的协同过滤推荐技术在推荐准确性上有较大提高。

Collaborative filtering recommendation model based on trust

The traditional collaborative filtering technology carries on the recommendation mainly based on user-item dataset, and cannot efficiently use the contextual information of user communication, thereby the recommended accuracy is further constrained. Aiming at the shortcomings of traditional collaborative filtering recommendation algorithm, in this paper, it fuses communication contextual information into the collaborative filtering algorithm, and introduces three types trust including communication trust, similarity trust and transmission trust, and a trust-based collaborative filtering recommendation model is also proposed. Experiments on the public dataset demonstrate that the recommendation algorithm outperforms the traditional collaborative algorithm.