Crossing Recommendation Based on Multi-B2C Behavior

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.