基于矩阵多源加权关联规则在个性化推荐中的应用

Based on matrix multi-source weighted association rules applied in personalized recommendation

摘要:

针对传统关联规则挖掘算法中频繁信息不完善,以及电子商务中各个影响因素贡献值不同的问题,提出一种基于矩阵多源加权关联个性化推荐模型——MSWPR模型,并在考虑虚拟行为水平加权和多源关联垂直加权的基础上,引入最小支持数概念作为剪枝的依据,进一步结合该模型对个性化推荐流程进行了概述。

Abstract:

In view of the traditional association rule mining algorithm of frequent information is imperfect,and the contribution of various factors in the e-commerce is different,this paper put forward a personalized recommendation model based on matrix multi-source weighted association---MSWPR model.And considering the virtual behavior level weighted and multi-source vertical weighted on the basis of correlation,a concept of minmum support count is introduced as the basis of pruning.Further combining with the model has summarized the personalized recommendation process.