research-article

Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce

Authors:

Wai LamAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 255 - 264

https://doi.org/10.1145/3340531.3411904

Published: 19 October 2020 Publication History

Abstract

Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.

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Cited By

Prakash YNayak ASunkara MJayarathna SLee HAshok V(2024)All in One Place: Ensuring Usable Access to Online Shopping Items for Blind UsersProceedings of the ACM on Human-Computer Interaction10.1145/36646398:EICS(1-25)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3664639
Huang YWang JWang SZhang RWang Q(2024)Which API is Faster: Mining Fine-grained Performance Opinion from Online Discussions2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS62785.2024.00066(608-619)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS62785.2024.00066
Huang TRen YWu LZhang X(2024)Reference-free review-based product question answering evaluation via distant contrastive learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10649987(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10649987
Show More Cited By

Index Terms

Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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October 19 - 23, 2020

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Cited By

Prakash YNayak ASunkara MJayarathna SLee HAshok V(2024)All in One Place: Ensuring Usable Access to Online Shopping Items for Blind UsersProceedings of the ACM on Human-Computer Interaction10.1145/36646398:EICS(1-25)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3664639
Huang YWang JWang SZhang RWang Q(2024)Which API is Faster: Mining Fine-grained Performance Opinion from Online Discussions2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS62785.2024.00066(608-619)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS62785.2024.00066
Huang TRen YWu LZhang X(2024)Reference-free review-based product question answering evaluation via distant contrastive learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10649987(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10649987
El Koufi N(2024)Toward a Recommender System for Assisting Customers at Risk of Churning in E-commerce Platforms Based on a Combination of Social Network Analysis (SNA) and Deep LearningJournal of Open Innovation: Technology, Market, and Complexity10.1016/j.joitmc.2024.100425(100425)Online publication date: Nov-2024
https://doi.org/10.1016/j.joitmc.2024.100425
Cao CFang XLuo BXia B(2024)SSR-TA: Sequence-to-Sequence-based expert recurrent recommendation for ticket automationNeural Computing and Applications10.1007/s00521-023-09152-136:4(1815-1832)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00521-023-09152-1
Liang JLi MWang JMa Y(2023)The Influencing Factors of the Helpfulness of User-Generated Product Q&AsSage Open10.1177/2158244023121911213:4Online publication date: 23-Dec-2023
https://doi.org/10.1177/21582440231219112
Ghasemi NAliannejadi MBonab HKanoulas Ede Vries AAllan JHiemstra DChen HDuh WHuang HKato MMothe JPoblete B(2023)Cross-Market Product-Related Question AnsweringProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591658(1293-1302)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591658
Zhang YWang MYu J(2023)Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00094(848-857)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00094
Du YJin XYan RYan J(2023)Sentiment enhanced answer generation and information fusing for product-related question answeringInformation Sciences10.1016/j.ins.2023.01.098627(205-219)Online publication date: May-2023
https://doi.org/10.1016/j.ins.2023.01.098
Deng YLi YZhang WDing BLam W(2022)Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference ModelingACM Transactions on Information Systems10.1145/350778240:4(1-28)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3507782
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