融合信任用户的协同过滤推荐算法

推荐系统中普遍存在的数据稀疏性问题使得协同过滤算法所要求的近邻搜索准确性降低，以及搜索到的最近邻用户过少，这对整个推荐系统的推荐质量和推荐的准确性产生重要影响，而这个问题对于传统的协同过滤推荐是难以解决的.针对这个问题，通过将用户之间的信任关系与对项目的评分相似性相融合，提出一种融合信任用户的协同过滤推荐算法，利用有向网络图构建的用户之间的信任关系，弥补了仅仅依靠计算用户间相似性不能准确衡量用户之间关系的缺陷.实验结果证明，该算法能够提高系统的推荐质量和准确性.

Collaborative Filtering Recommendation Algorithm Based on Trust Users

The common data sparsity in recommendation systems makes the nearest neighbor search is not accurate and lets the search results of the nearest neighbor is too small. This will affect the recommended quality and accuracy of the recommendation system, moreover it is difficult to solve in the traditional collaborative filtering recommendation. To overcome the difficulty of data sparsity in recommendation systems, a novel collaborative filtering algorithm is presented which is based on the combination of trust relationship between users and the similarity of scores of the projects. This algorithm constructs the trust relationship among users by using a directed network graph, which can make up the defect that the user''s relationship cannot be accurately measured by the user''s similarity. The experimental results show that the proposed algorithm can improve the quality and accuracy of the recommendation system.