关键词: 推荐系统技术, 协同过滤, 改进模糊划分, 模糊[C]均值聚类

Abstract: The traditional Collaborative Filtering（CF） recommendation algorithm has the defects of sparse score matrix, weak extensibility and low recommendation accuracy. A collaborative filtering recommendation algorithm（GIFP-CCF+） is proposed to improve the fuzzy partition clustering. In the traditional calculation method based on modified cosine similarity, the time-difference factor, hot-item weight factor and cold-item weight factor are introduced to improve the similarity calculation results. At the same time, the GIFP-FCM algorithm which improves the fuzzy partition is introduced to form a class of items with similar attributes, construct index matrix, and base on the index. The similarity between items finds the nearest neighbor recommendation of the project, thereby improving the accuracy of the Collaborative Filtering algorithm（CF）. By comparing with Kmeans-CF, FCM-CF and GIFP-CCF algorithms, it is proved that the GIFP-CCF+ algorithm has some advantages in recommendation result and recommendation precision.

Key words: recommender system, collaborative filtering, improved fuzzy partition, fuzzy C means clustering