Discovering Relevant Reviews for Answering Product-related Queries

被引：10

作者：

Zhang, Shiwei ^{[1
,3
]}

Lau, Jey Han ^{[2
]}

Zhang, Xiuzhen ^{[1
]}

Chan, Jeffrey ^{[1
]}

Paris, Cecile ^{[3
]}

机构：

[1] RMIT Univ, Melbourne, Vic, Australia

[2] Univ Melbourne, Melbourne, Vic, Australia

[3] CSIRO Data61, Canberra, ACT, Australia

来源：

2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019) | 2019年

关键词：

Product Question Answering; Mixtures of Experts; Deep Learning;

D O I：

10.1109/ICDM.2019.00192

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the increasing popularity of e-commerce, the number of product-related queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic question-answering systems can be immensely helpful. Product queries are, however, very different from open-domain questions: they tend to be product-specific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming state-of-the-art systems by consistently finding the most relevant reviews for product queries.

引用

页码：1468 / 1473

页数：6