关联集合在协同过滤推荐算法中的应用研究

摘要

In order to improve the precision of electronic commerce recommendation system,this paper puts forward collaborative filtering algorithm based on associative sets.It uses frequent itemsets to get associative sets,and makes recommendations according to users＇ real preferences,so as to enhance the accuracy of recommending results.This new algorithm is more accurate than the traditional method in recommendation precision.%为了提高电子商务推荐系统的精度,提出了基于关联集合的协同过滤推荐算法,该算法通过频繁项集生成算法生成一系列频繁项集,然后通过合并处理过滤掉与用户关联很小的一些噪音项目,从而使协同过滤算法更加有效。该算法在推荐精度上比传统的方法优越。