Study on personal preferences mining method in O2O E-commerce model

Shang Peini and Yuan Feiyun

Abstract

In this paper, we studied the theory of personalized recommendation, compared the different recommendation technologies, and analyzed the applicability of several recommendation technologies to the O2O mode of e-commerce. And then an approach for mining personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems was proposed. The experiment results show that the recommendation method put forward in the thesis is able to meet the demand of the O2O e-commerce mode.