AliISA: Creating an Interactive Search Experience in E-commerce Platforms

被引：0

作者：

Xiao, Fei ^{[1
]}

Wang, Zhen ^{[1
]}

Huang, Haikuan ^{[1
]}

Huang, Jun ^{[1
]}

Chen, Xi ^{[1
]}

Deng, Hongbo ^{[1
]}

Qiu, Minghui ^{[1
]}

Gong, Xiaoli ^{[2
]}

机构：

[1] Alibaba Grp, Hangzhou, Zhejiang, Peoples R China

[2] Nankai Univ, Tianjin, Peoples R China

来源：

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) | 2019年

基金：

中国国家自然科学基金;

关键词：

information retrieval; reinforcement learning;

D O I：

10.1145/3331184.3331409

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1305 / 1308

页数：4

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