计算机技术与发展Issue(1)：22-26,32,6.DOI:10.3969/j.issn.1673-629X.2016.01.005
基于模糊C均值聚类有效性的协同过滤算法
A Collaborative Filtering Algorithm Based on Fuzzy C-means Clustering Validity
摘要
Abstract
Considering the sparsity and the scalability of traditional collaborative filtering recommendation algorithms in electronic com-merce system,a new collaborative filtering algorithm is presented based on fuzzy C-means clustering validity. Firstly,a reasonable cluster number range is presetted,and then an optimal cluster number is determined based on some representative fuzzy clustering validity func-tions and Xie-Beni method. Secondly,using the optimal number of cluster,this algorithm transforms the users’ preferences of single item to similar groups with fuzzy C-means clustering,and sparse user-item preferences is established to dense user-fuzzy preferences. Finally, according to the item’s cluster it finds the nearest neighbors of the object user and generates recommendations. The experimental results in MovieLens show that the new algorithm improves recommendation quality in MAE,recall and coverage.关键词
协同过滤/模糊C均值聚类算法/聚类有效性函数/最佳聚类簇数Key words
collaborative filtering/fuzzy C-means clustering/clustering validity/optimal number of clustering分类
信息技术与安全科学引用本文复制引用
葛林涛,徐桂琼..基于模糊C均值聚类有效性的协同过滤算法[J].计算机技术与发展,2016,(1):22-26,32,6.基金项目
国家自然科学基金资助项目(11201290,61104042) （11201290,61104042）
