CAJ | 학술논문

利用百分点科技推荐引擎提供的原始数据,分析了用户跨电商的行为,提出了一种可在多个电商之间进行交叉推荐的算法.结果证明,该算法不仅在精确性上较完全冷启动的随机推荐有巨大的提高,而且所推荐的商品可以保持相当的多样性与新颖性.分析显示有约5%~10%的点击、收藏和购买行为发生在有交叉行为的用户身上,这些用户的活跃性明显强于非交叉用户.这些结果暗示交叉用户可能是网上购物的重度用户.该文展现了全新的研究思路,研讨了全新的分析对象,其思路和结果对于电子商务研究有重要价值.
이용백분점과기추천인경제공적원시수거,분석료용호과전상적행위,제출료일충가재다개전상지간진행교차추천적산법.결과증명,해산법불부재정학성상교완전랭계동적수궤추천유거대적제고,이차소추천적상품가이보지상당적다양성여신영성.분석현시유약5%~10%적점격、수장화구매행위발생재유교차행위적용호신상,저사용호적활약성명현강우비교차용호.저사결과암시교차용호가능시망상구물적중도용호.해문전현료전신적연구사로,연토료전신적분석대상,기사로화결과대우전자상무연구유중요개치.
Personalized recommendation has now been widely used in E-commerce, but there are still some problems to be solved such as cold-start problem, data sparsity, diversity-accuracy dilemma and so on. Existing literatures have focused on single data set, lacking a systematic understanding about the accessing behavior involving multiple web sites. Thanks to the real data, provided by Baifendian Information Technology recommendation engine, we analyze users' behavior on multi-B2Cs (business-to-customers) and propose a crossing recommendation algorithm which is able to recommend items of a B2C site to users according to the records of users in other B2C web sites. This algorithm largely improves accuracy compared with purely random recommendation under completely cold-start environment and can still keep high diversity and novelty.