关键词: 协同过滤, 相似性反馈机制, 平均绝对误差, 平均评分时间, 平均推荐时间

Abstract: Traditional memory-based collaborative filtering algorithm has the problem of bad scalability，while the model-based collaborative filtering algorithm，due to lagged updating hysterics，has the problem of bad recommendation. To solve the above problems，a collaborative filtering algorithm based on real-time users feedback was proposed，which achieved that recommender system can finish the real-time updating of the model data when a new rating was submitted by active user. Hence, recommender system can reflect the changing of user interest accurately. The experimental results indicate that the algorithm can improve the recommendation accuracy efficiently and reduce the recommendation time significantly.

Key words: collaborative filtering, similarity feedback mechanism, mean absolute error, mean access time, mean recommended time