research-article

ProductQnA: Answering User Questions on E-Commerce Product Pages

Authors:

Ashish Kulkarni,

Nikhil Rasiwasia,

Srinivasan SengameduAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 354 - 360

https://doi.org/10.1145/3308560.3316597

Published: 13 May 2019 Publication History

Abstract

Product pages on e-commerce websites often overwhelm their customers with a wealth of data, making discovery of relevant information a challenge. Motivated by this, here, we present a novel framework to answer both factoid and non-factoid user questions on product pages. We propose several question-answer matching models leveraging both deep learned distributional semantics and semantics imposed by a structured resource like a domain specific ontology. The proposed framework supports the use of a combination of these models and we show, through empirical evaluation, that a cascade of these models does much better in meeting the high precision requirements of such a question-answering system. Evaluation on user asked questions shows that the proposed system achieves 66% higher precision1 as compared to IDF-weighted average of word vectors baseline [1].

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Cited By

Sheng CLuo LZhang T(2024)Research on e-commerce product evaluation based on multi-source heterogeneous data fusionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675500(500-504)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675500
Jin XDu YWang BZhang Q(2024)Cross-biased Contrastive Learning for Answer Selection with Dual-Tower StructureNeurocomputing10.1016/j.neucom.2024.128641(128641)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128641
Zhang YLau RDavid Xu JRao YLi Y(2024)Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directionsArtificial Intelligence Review10.1007/s10462-024-10744-z57:5Online publication date: 11-Apr-2024
https://doi.org/10.1007/s10462-024-10744-z
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Index Terms

ProductQnA: Answering User Questions on E-Commerce Product Pages

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

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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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

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Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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

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Cited By

Sheng CLuo LZhang T(2024)Research on e-commerce product evaluation based on multi-source heterogeneous data fusionProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675500(500-504)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675500
Jin XDu YWang BZhang Q(2024)Cross-biased Contrastive Learning for Answer Selection with Dual-Tower StructureNeurocomputing10.1016/j.neucom.2024.128641(128641)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128641
Zhang YLau RDavid Xu JRao YLi Y(2024)Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directionsArtificial Intelligence Review10.1007/s10462-024-10744-z57:5Online publication date: 11-Apr-2024
https://doi.org/10.1007/s10462-024-10744-z
Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_3
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
Feine JMorana SMaedche A(2022)Intrance: Designing an Interactive Enhancement System for the Development of QA ChatbotsProceedings of the ACM on Human-Computer Interaction10.1145/35551996:CSCW2(1-24)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555199
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
Gangadhar GKulkarni A(2022)Extraction of Product Specifications from the Web - Going Beyond Tables and ListsProceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)10.1145/3493700.3493713(19-27)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3493700.3493713
Kim EYoon HLee JKim M(2022)Accurate and prompt answering framework based on customer reviews and question-answer pairsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117405203:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117405
Zalmout NZhang CLi XLiang YDong XZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)All You Need to Know to Build a Product Knowledge GraphProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3470825(4090-4091)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3470825
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