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research-article

Review Response Generation in E-Commerce Platforms with External Product Information

Authors:

Xuanjing Huang,

Xiaozhong LiuAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 2425 - 2435

https://doi.org/10.1145/3308558.3313581

Published: 13 May 2019 Publication History

Abstract

''User reviews” are becoming an essential component of e-commerce. When buyers write a negative or doubting review, ideally, the sellers need to quickly give a response to minimize the potential impact. When the number of reviews is growing at a frightening speed, there is an urgent need to build a response writing assistant for customer service providers. In order to generate high-quality responses, the algorithm needs to consume and understand the information from both the original review and the target product. The classical sequence-to-sequence (Seq2Seq) methods can hardly satisfy this requirement. In this study, we propose a novel deep neural network model based on the Seq2Seq framework for the review response generation task in e-commerce platforms, which can incorporate product information by a gated multi-source attention mechanism and a copy mechanism. Moreover, we employ a reinforcement learning technique to reduce the exposure bias problem. To evaluate the proposed model, we constructed a large-scale dataset from a popular e-commerce website, which contains product information. Empirical studies on both automatic evaluation metrics and human annotations show that the proposed model can generate informative and diverse responses, significantly outperforming state-of-the-art text generation models.

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Cited By

Yuan YGong CKong DYu NFu G(2023)Multi-grained Aspect Fusion for Review Response GenerationArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44201-8_3(25-37)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44201-8_3
Chen BLiu JMaimaiti MGao XZhang JAl Hasan MXiong L(2022)Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerceProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557122(2994-3002)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557122
Sun TWang CSong XFeng FNie L(2022)Response Generation by Jointly Modeling Personalized Linguistic Styles and EmotionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347587218:2(1-20)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3475872
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Published In

cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yuan YGong CKong DYu NFu G(2023)Multi-grained Aspect Fusion for Review Response GenerationArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44201-8_3(25-37)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44201-8_3
Chen BLiu JMaimaiti MGao XZhang JAl Hasan MXiong L(2022)Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerceProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557122(2994-3002)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557122
Sun TWang CSong XFeng FNie L(2022)Response Generation by Jointly Modeling Personalized Linguistic Styles and EmotionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347587218:2(1-20)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3475872
Yuan YXu FCao HZhang GHui PLi YJin D(2022)Persuade to Click: Context-aware Persuasion Model for Online Textual AdvertisementIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3110724(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2021.3110724
He SZhang HTu ZChu D(2022)Personalized Review Recommendation without User Interactive Data2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00307(2062-2070)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00307
Cheng FThongma WWu C(2022)The Influence of Website Social Cues: A Cross-Culture ComparisonJournal of Computer Information Systems10.1080/08874417.2022.206164063:2(351-368)Online publication date: 20-Apr-2022
https://doi.org/10.1080/08874417.2022.2061640
Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Cai Z(2021)Generating Explanations for Recommendation Systems via Injective VAE2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00115(1012-1017)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00115
Wang YSong KBing LLiu X(2021)Harvest shopping advice: Neural Question Generation from multiple information sources in E-commerceNeurocomputing10.1016/j.neucom.2020.12.013433(252-262)Online publication date: Apr-2021
https://doi.org/10.1016/j.neucom.2020.12.013
Shraga RRoitman HFeigenblat GCannim MHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Web Table Retrieval using Multimodal Deep LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401120(1399-1408)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401120
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