关键词: 协同过滤, 标签, 指数渐进遗忘函数, 时间窗口, 准确率

Abstract: In view of the existing personalized recommendation system based on tags in the construction of user interest model which does not both fully mine user’s real interest and take into account the impact of the time factor on the recommendation results, an improved collaborative filtering algorithm based on tags（ITCF） is proposed. The algorithm integrates user’s ratings into calculation of the weight of tags, considers the impact of user’s different interest on the results of the project, and uses a combination of exponential gradual forgetting function and time window to capture the changes of user’s interest. Experiments on the Movielens data set show that the improved algorithm has better recommendation effects on three evaluation metrics which are precision, hit-rank and NDCG, the quality and effectiveness of the recommendation are both better than the traditional method.

Key words: collaborative filtering, tags, exponential gradually forgotten function, time window, precision