基于跨电商行为的交叉推荐算法  

Crossing Recommendation Based on Multi-B2C Behavior

作　　者：张亮[1,2] 柏林森 周涛[2,3] 

机构地区：[1]北京百分点信息科技有限公司,北京海淀区100080 [2]电子科技大学互联网科学中心,成都610054 [3]杭州师范大学阿里巴巴商学院,杭州310036

出　　处：《电子科技大学学报》2013年第1期154-160,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金面上项目(60973069);中央高校基本科研业务费(ZYGX2010Z001)

摘　　要：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。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.

关 键 词：冷启动问题 交叉推荐 电子商务 跨电商行为 推荐系统 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]