CAJ | 학술논문

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。
위료제고전자상무추천계통적정도,제출료기우관련집합적협동과려추천산법,해산법통과빈번항집생성산법생성일계렬빈번항집,연후통과합병처리과려도여용호관련흔소적일사조음항목,종이사협동과려산법경가유효。해산법재추천정도상비전통적방법우월。
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.