计算机工程与应用2019，Vol.55Issue(5)：118-123,6.DOI:10.3778/j.issn.1002-8331.1804-0318
改进模糊划分聚类的协同过滤推荐算法
Collaborative Filtering Recommendation Algorithm Based on Improved Fuzzy Partition Clustering
摘要
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.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Key words
recommender system/ collaborative filtering/ improved fuzzy partition/ fuzzy C means clustering分类
信息技术与安全科学引用本文复制引用
苏庆,章静芳,林正鑫,李小妹,蔡昭权,曾永安..改进模糊划分聚类的协同过滤推荐算法[J].计算机工程与应用,2019,55(5):118-123,6.基金项目
国家自然科学基金(No.61303079). （No.61303079）
