research-article

Recommending Answerers for Stack Overflow with LDA Model

Authors:

Jiafei YanAuthors Info & Claims

ChineseCSCW '17: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing

Pages 80 - 86

https://doi.org/10.1145/3127404.3127426

Published: 22 September 2017 Publication History

Abstract

Stack Overflow is the largest Community-based Question Answering (CQA) site for software developers. Its popularity is mainly attributed to the timely answers provided by a great number of developers. When having problems in learning and using new technologies, developers resort to Stack Overflow for help. However, it is difficult to recommend questions to the potential answerers due to the huge numbers of questions. In order to improve the accuracy of question recommending, we need to find out the members who are interested in the fields related to the questions and match the ability of developers with the difficulty of questions. To do so, we need to pay close attention to the behavior of developers. This paper presents a model with two prediction approaches, namely, the traditional feature-based approach and LDA (Latent Dirichlet Allocation) based approach. When a new question arrives, this model will use LDA method to label the question and indicate the proper category to which the question belongs according to latent semantic feature and content feature. Then, with the traditional features of the question and the asker information, the model will recommend the appropriate developers to answer this new question.

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Cited By

Menaha RJayanthi V(2023)Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain KnowledgeIETE Journal of Research10.1080/03772063.2023.218123370:3(2602-2614)Online publication date: 23-Feb-2023
https://doi.org/10.1080/03772063.2023.2181233
Hadi Mogavi RHaq EGujar SHui PMa X(2022)More Gamification Is Not Always BetterProceedings of the ACM on Human-Computer Interaction10.1145/35555536:CSCW2(1-32)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555553
Zhao MJia A(2022)A Dual-Attention Heterogeneous Graph Neural Network for Expert Recommendation in Online Agricultural Question and Answering Communities2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776176(926-931)Online publication date: 4-May-2022
https://doi.org/10.1109/CSCWD54268.2022.9776176
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Index Terms

Recommending Answerers for Stack Overflow with LDA Model
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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ChineseCSCW '17: Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing

September 2017

269 pages

ISBN:9781450353526

DOI:10.1145/3127404

General Chairs:
Zili Zhang,
Ning Gu,
Program Chairs:
Shaozi Li,
Tun Lu,
Li Li

Copyright © 2017 ACM.

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Publication History

Published: 22 September 2017

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ChineseCSCW '17

ChineseCSCW '17: Chinese Conference on Computer Supported Cooperative Work and Social Computing

September 22 - 23, 2017

Chongqing, China

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ChineseCSCW '17 Paper Acceptance Rate 21 of 84 submissions, 25%;

Overall Acceptance Rate 21 of 84 submissions, 25%

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Cited By

Menaha RJayanthi V(2023)Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain KnowledgeIETE Journal of Research10.1080/03772063.2023.218123370:3(2602-2614)Online publication date: 23-Feb-2023
https://doi.org/10.1080/03772063.2023.2181233
Hadi Mogavi RHaq EGujar SHui PMa X(2022)More Gamification Is Not Always BetterProceedings of the ACM on Human-Computer Interaction10.1145/35555536:CSCW2(1-32)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555553
Zhao MJia A(2022)A Dual-Attention Heterogeneous Graph Neural Network for Expert Recommendation in Online Agricultural Question and Answering Communities2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776176(926-931)Online publication date: 4-May-2022
https://doi.org/10.1109/CSCWD54268.2022.9776176
Georgiou KMittas NChatzigeorgiou AAngelis L(2022)An empirical study of COVID-19 related posts on Stack OverflowJournal of Systems and Software10.1016/j.jss.2021.111089182:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111089
Kumar SChauhan A(2022)Augmenting Textbooks with cQA Question-Answers and Annotated YouTube Videos to Increase Its RelevanceNeural Processing Letters10.1007/s11063-022-10897-455:1(551-588)Online publication date: 30-Jun-2022
https://doi.org/10.1007/s11063-022-10897-4
Song JXu XWang X(2021)TSAR-based Expert Recommendation Mechanism for Community Question Answering2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437843(162-167)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437843
Lin CWu YChen A(2021)Selecting the most helpful answers in online health question answering communitiesJournal of Intelligent Information Systems10.1007/s10844-021-00640-1Online publication date: 18-May-2021
https://doi.org/10.1007/s10844-021-00640-1
Nishinaka RUbayashi NKamei YSato R(2020)How Fast and Effectively Can Code Change History Enrich Stack Overflow?2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS51102.2020.00066(467-478)Online publication date: Dec-2020
https://doi.org/10.1109/QRS51102.2020.00066
Alshangiti MSapkota HMurukannaiah PLiu XYu Q(2019)Why is Developing Machine Learning Applications Challenging? A Study on Stack Overflow Posts2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1109/ESEM.2019.8870187(1-11)Online publication date: Sep-2019
https://doi.org/10.1109/ESEM.2019.8870187

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