Visual potential expert prediction in question and answering communities. (October 2018)

• Record Type: Journal Article
• Authors: Xiong, Xiaoxiao; Fu, Min; Zhu, Min; Liang, Jing
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 70
• Page End: 80
• Publication Date: 2018-10
• Subjects: Question and answering communities -- Potential experts prediction -- Interactive machine learning -- Visual analysis; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.03.001
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: The success of Question and Answering (Q&A) communities mainly depends on the contribution of experts. However, there is a bottleneck for machine to identify these experts as soon as they participate in a community due to lack of enough activities during users' early participation. To tackle that, we bring human's business experience to potential expert prediction by combining machine learning and visual analytics. In this work, we propose a visual analytics system to identify potential experts semi-automatically. After the machine learning algorithm gives the result of the expert probability, analysts can locate a set of interested users whose expert probability is ambiguous and check the user information and behavior patterns of those users via the design of multi-dimension data visualization. Finally, our system models analysts' knowledge of the community members' identities, and then abstracts the knowledge quantificationally for machine learning algorithm. Thus, analysts can modify machine learning algorithm and the prediction process smoothly. A quantitative evaluation with real data has been studied to demonstrate the effectiveness of our system.