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Expert finding in community question answering: a review

Expert finding in community question answering: a review
Expert finding in community question answering: a review
The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.
Expert finding, Matrix factorization, Deep Learning, Ensemble Learning
0269-2821
1-32
Yuan, Sha
38f55c31-5145-40da-9777-d9ca2219c3e0
Zhang, Yu
9b5536fe-d7c1-40a1-b3f5-c0cc6f0724e7
Tang, Jie
69c44bae-b1fa-45eb-a01d-3ac5b00fa749
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Bautista Cabota, Juan
c6f7d019-efb2-4f27-8181-0ef639d558a3
Yuan, Sha
38f55c31-5145-40da-9777-d9ca2219c3e0
Zhang, Yu
9b5536fe-d7c1-40a1-b3f5-c0cc6f0724e7
Tang, Jie
69c44bae-b1fa-45eb-a01d-3ac5b00fa749
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Bautista Cabota, Juan
c6f7d019-efb2-4f27-8181-0ef639d558a3

Yuan, Sha, Zhang, Yu, Tang, Jie, Hall, Wendy and Bautista Cabota, Juan (2019) Expert finding in community question answering: a review. Artificial Intelligence Review, 1-32. (doi:10.1007/s10462-018-09680-6).

Record type: Article

Abstract

The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.

Text
1804.07958 - Accepted Manuscript
Restricted to Repository staff only until 19 March 2020.
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More information

e-pub ahead of print date: 19 March 2019
Keywords: Expert finding, Matrix factorization, Deep Learning, Ensemble Learning

Identifiers

Local EPrints ID: 429729
URI: https://eprints.soton.ac.uk/id/eprint/429729
ISSN: 0269-2821
PURE UUID: 68a567cf-081e-4f96-ad45-ff5ec41fca92
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811

Catalogue record

Date deposited: 04 Apr 2019 16:30
Last modified: 03 Dec 2019 02:08

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Contributors

Author: Sha Yuan
Author: Yu Zhang
Author: Jie Tang
Author: Wendy Hall ORCID iD
Author: Juan Bautista Cabota

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