research-article

Utilizing Non-QA Data to Improve Questions Routing for Users with Low QA Activity in CQA

Authors:

Maria BielikovaAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 129 - 136

https://doi.org/10.1145/2808797.2809331

Published: 25 August 2015 Publication History

Abstract

Community Question Answering (CQA) systems, such as Yahoo! Answers and Stack Overflow, represent a well-known example of collective intelligence. The existing CQA systems, despite their overall successfulness and popularity, fail to answer a significant amount of questions in required time. One option for scaffolding collaboration in CQA systems is a recommendation of new questions to users who are suitable candidates for providing correct answers (so called question routing). Various methods have been proposed so far to find appropriate answerers, but almost all approaches heavily depend on previous users' activities in a particular CQA system (i.e. QA-data). In our work, we attempt to involve a whole community including users with no or minimal previous activity (e.g. newcomers or lurkers). We proposed a question routing method which analyses users' non-QA data from a CQA system itself as well as from external services and platforms, such as blogs, microblogs or social networking sites, in order to estimate users' interests and expertise early and more precisely. Consequently, we can recommend new questions to a wider part of a community as well as more accurately. Evaluation on a dataset from Stack Exchange platform showed that considering non-QA data leads not only to better recognition of users with low activity as suitable answerers, but also to higher overall precision of the recommendations. It implies that non-QA data can supplement QA data during expertise estimation in question routing and thus also improve a success rate of a questions answering process.

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Cited By

Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Roy PSaumya SSingh JBanerjee SGutub A(2022)Analysis of community question‐answering issues via machine learning and deep learningCAAI Transactions on Intelligence Technology10.1049/cit2.120818:1(95-117)Online publication date: 4-May-2022
https://dl.acm.org/doi/10.1049/cit2.12081
Kundu DPal RMandal D(2021)Time-aware hybrid expertise retrieval system in community question answering servicesApplied Intelligence10.1007/s10489-020-02177-2Online publication date: 17-Feb-2021
https://doi.org/10.1007/s10489-020-02177-2
Show More Cited By

Utilizing Non-QA Data to Improve Questions Routing for Users with Low QA Activity in CQA
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 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: 25 August 2015

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ASONAM '15

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ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Roy PSaumya SSingh JBanerjee SGutub A(2022)Analysis of community question‐answering issues via machine learning and deep learningCAAI Transactions on Intelligence Technology10.1049/cit2.120818:1(95-117)Online publication date: 4-May-2022
https://dl.acm.org/doi/10.1049/cit2.12081
Kundu DPal RMandal D(2021)Time-aware hybrid expertise retrieval system in community question answering servicesApplied Intelligence10.1007/s10489-020-02177-2Online publication date: 17-Feb-2021
https://doi.org/10.1007/s10489-020-02177-2
Santos TWalk SKern RStrohmaier MHelic D(2019)Activity Archetypes in Question-and-Answer (Q8A) Websites—A Study of 50 Stack Exchange InstancesACM Transactions on Social Computing10.1145/33016122:1(1-23)Online publication date: 21-Feb-2019
https://dl.acm.org/doi/10.1145/3301612
Kumar NG SMudda KTrishal GKonjengbam ASingh MDey LChaudhury SKrishnapuram RSingla PRoy R(2019)Where to PostProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297018(136-142)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1145/3297001.3297018
Yang ZLiu QSun BZhao X(2019)Expert recommendation in community question answering: a review and future directionInternational Journal of Crowd Science10.1108/IJCS-03-2019-00113:3(348-372)Online publication date: 2-Sep-2019
https://doi.org/10.1108/IJCS-03-2019-0011
Chi RWu BWang L(2018)Expert Identification Based on Dynamic LDA Topic Model2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2018.00141(881-888)Online publication date: Jun-2018
https://doi.org/10.1109/DSC.2018.00141
Wang XHuang CYao LBenatallah BDong M(2018)A Survey on Expert Recommendation in Community Question AnsweringJournal of Computer Science and Technology10.1007/s11390-018-1845-033:4(625-653)Online publication date: 13-Jul-2018
https://doi.org/10.1007/s11390-018-1845-0
Le LShah C(2018)Retrieving people: Identifying potential answerers in Community Question‐AnsweringJournal of the Association for Information Science and Technology10.1002/asi.2404269:10(1246-1258)Online publication date: 18-Jul-2018
https://doi.org/10.1002/asi.24042
Macina JSrba IWilliams JBielikova MCremonesi PRicci FBerkovsky STuzhilin A(2017)Educational Question Routing in Online Student CommunitiesProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109886(47-55)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109886
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