摘要
如何根据用户当前的访问行为,预测他下一个感兴趣的商品,做出针对性的推荐成为电子商务的一个重要研究内容。文章提出了一种不需产生大量非频繁项集的关联规则挖掘算法,该算法利用相关性很好的改善了经典Apriori算法中存在大量冗余规则问题。最后通过实验证明了算法的有效性。
It is an important e-commerce research field to predict a user＇s next commodity of interest and then,to recommend a specific based on his current visit behavior.In this paper,an association rule mining algorithm that does not need to produce a large number of infrequent item sets is proposed.By using the correlation of a rule,a good solution can be made to the problem of the existence of a large number of redundant rules in the classic Apriori algorithm.Finally,the effectiveness of the algorithm is proved by experiments.
出处
《计算机与数字工程》
2012年第6期148-150,共3页
Computer & Digital Engineering
关键词
负关联规则
电子商务
相关性
negative association rules
e-commerce
commodity recommend
correlation
