计算机应用研究2018，Vol.35Issue(1)：105-108,112,5.DOI:10.3969/j.issn.1001-3695.2018.01.021
一种改进的top-N协同过滤推荐算法
Improved top-N collaborative filtering recommendation algorithm
摘要
Abstract
There exists several issues in traditional collaborative filtering algorithms:a)It takes the impact of all users' historical feedback information into account when calculating the similarities between any two items;b)It only utilizes the user's rating data when calculating the similarities.However,the user group that has similar interests with the target user has a higher reference value than other users.Considering the fact that irrelevant historical information leaded to poor recommendation results,this paper proposed a novel collaborative filtering recommendation algorithm based on K-means clustering.The new algorithm refined the user's similarity metric with the user's common rating weight and popular items weight,the item's similarity metric with time difference weight and user's rating weight respectively,and clustered all uses into several partitions according to the similarities.Then,it applied recommend algorithm in each of the clusters.Experimental results show that,compared with traditional item-based top-N collaborative filtering recommendation algorithm,the proposed algorithm can improve the recall by 2.1％ on average.The proposed algorithm can improve the accuracy and the quality of the recommendation effectively.关键词
协同过滤推荐算法/用户评分信息/相似度/聚类算法/召回率Key words
collaborative filtering recommendation algorithm/wser's rating information/similarity/clustering algorithm/recall分类
信息技术与安全科学引用本文复制引用
肖文强,姚世军,吴善明..一种改进的top-N协同过滤推荐算法[J].计算机应用研究,2018,35(1):105-108,112,5.
