基于用户特征的协同过滤推荐算法

协同过滤算法是目前在电商系统中应用最广的推荐技术.为了缓解传统基于用户的协同过滤算法在冷启动、推荐准确性和数据稀疏性方面的缺点，本文提出基于用户特征的协同过滤推荐算法.此算法利用注册信息提取属性特征，并对已有的评分信息提取兴趣特征和信任度，综合以上各特征融合特征相似性进一步产生推荐.实验结果表明，与传统的基于用户的协同过滤算法做对比，基于用户特征的协同过滤算法对推荐的精度有大幅的提高.

Collaborative Filtering Recommendation Algorithm Based on User Features

Collaborative filtering algorithm is the most widely used recommendation technology in e-commerce system. In order to alleviate the shortcomings of traditional user-based collaborative filtering algorithm in cold start, recommendation accuracy, and data sparsity, this study proposes collaborative filtering recommendation algorithm based on user characteristics. This algorithm extracts the attribute features by using the registration information, extracts the interest features and trust degree from the existing scoring information, and synthesizes the feature similarity of the above features to further generate recommendations. Experimental results show that comparing with the traditional user-based collaborative filtering algorithm, the collaborative filtering algorithm based on user characteristics greatly improves the accuracy of the recommendation.