Creating an Interactive Search Experience in E-commerce Platforms

Fei Xiao, Zhen WANG, Haikuan Huang, Jun Huang, Xi Chen, Hongbo Deng, Minghui Qiu, Xiaoli Gong

Abstract:

Online shopping has been a habit of more and more people, while
most users are unable to craft an informative query, and thus it
often takes a long search session to satisfy their purchase intents.
We present AliISA—a shopping assistant which offers users some
tips to further specify their queries during a search session. With
such an interactive search, users tend to find targeted items with
fewer page requests, which often means a better user experience.
Currently, AliISA assists tens of millions of users per day, earns
more usage than existing systems, and consequently brings in a 5%
improvement in CVR. In this paper, we present our system, describe
the underlying techniques, and discuss our experience in stabilizing
reinforcement learning under an E-commerce environment.