融合用户信任模型的协同过滤推荐算法

协同过滤推荐是电子商务系统中最为重要的技术之一.随着电子商务系统中用户数目和商品数目的增加，用户-项目评分数据稀疏性问题日益显著.传统的相似度度量方法是基于用户共同评分项目计算的，而过于稀疏的评分使得不能准确预测用户偏好，导致推荐质量急剧下降.针对上述问题，本文考虑用户评分相似性和用户之间信任关系对推荐结果的影响，利用层次分析法实现用户信任模型的构建，提出一种融合用户信任模型的协同过滤推荐算法.实验结果表明： 该算法能够有效反映用户认知变化，缓解评分数据稀疏性对协同过滤推荐算法的影响，提高推荐结果的准确度.

Collaborative Filtering Recommendation Based on User Trust Model

Collaborative filtering is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, the problem of data sparsity of user project is becoming more and more significant. In traditional collaborative filtering recommender systems, similarity of users is often calculated based on common ratings. When user-item ratings are sparse, the accuracy of recommendations will be influenced because users with similar preferences can''t be found accurately. Considering the effect of users'' ratings and trusts on the recommendation results, this paper applies AHP to construct user trust model and proposes a collaborative filtering recommendation method combining user trust model. The experimental results show that, user similarity calculation method combining user trust can effectively reflect the user''s cognitive changes, ease the impact of data sparsity on the collaborative filtering recommendation algorithm and improve the accuracy of recommendation results.