APSIPA Transactions on Signal and Information Processing > Vol 10 > Issue 1

Toward community answer selection by jointly static and dynamic user expertise modeling

Yuchao Liu, Shandong University, China, Meng Liu, Shandong Jianzhu University, China, Jianhua Yin, Shandong University, China, jhyin@sdu.edu.cn
 
Suggested Citation
Yuchao Liu, Meng Liu and Jianhua Yin (2021), "Toward community answer selection by jointly static and dynamic user expertise modeling", APSIPA Transactions on Signal and Information Processing: Vol. 10: No. 1, e3. http://dx.doi.org/10.1017/ATSIP.2020.28

Publication Date: 01 Mar 2021
© 2021 Yuchao Liu, Meng Liu and Jianhua Yin
 
Subjects
 
Keywords
User expertisecommunity question answeringanswer selectiongraph neural network
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. RELATED WORK 
III. OUR PROPOSED MODEL 
IV. EXPERIMENTS 
V. CONCLUSION 

Abstract

Answer selection, ranking high-quality answers first, is a significant problem for the community question answering sites. Existing approaches usually consider it as a text matching task, and then calculate the quality of answers via their semantic relevance to the given question. However, they thoroughly ignore the influence of other multiple factors in the community, such as the user expertise. In this paper, we propose an answer selection model based on the user expertise modeling, which simultaneously considers the social influence and the personal interest that affect the user expertise from different views. Specifically, we propose an inductive strategy to aggregate the social influence of neighbors. Besides, we introduce the explicit topic interest of users and capture the context-based personal interest by weighing the activation of each topic. Moreover, we construct two real-world datasets containing rich user information. Extensive experiments on two datasets demonstrate that our model outperforms several state-of-the-art models.

DOI:10.1017/ATSIP.2020.28