关键词: 协同过滤, 推荐系统, 安全的半监督支持向量机（S4VM）, 半监督学习, 置信度

Abstract: To overcome the data sparseness and ignoring items, user feature of traditional collaborative filtering algorithm which can lead to the decrease of efficiency of recommendation, this paper proposes a collaborative filtering recommendation algorithm based on safe and high confident semi-supervised method S4VM to predict the rating of unrated items effectively. Through predicting the rating of unrated items, the sparseness of data has been alleviated and the accuracy of searching nearest neighbor item has been improved simultaneously. The experimental results show that the algorithm can improve the recommended quality of the system.

Key words: collaborative filtering, recommendation system, Safe Semi-Supervised Support Vector Machine（S4VM）, semi-supervised learning, confidence