通过静态和动态用户专业知识建模实现社区答案选择

Yuchao Liu, Meng Liu, Jianhua Yin
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引用次数: 1

摘要

答案选择,将高质量的答案排在首位,是社区问答网站面临的一个重大问题。现有的方法通常将其视为一项文本匹配任务,然后通过答案与给定问题的语义相关性来计算答案的质量。然而,他们完全忽略了社区中其他多种因素的影响,例如用户专业知识。在本文中,我们提出了一个基于用户专业知识模型的答案选择模型,该模型同时从不同角度考虑了影响用户专业知识的社会影响和个人兴趣。具体而言,我们提出了一种归纳策略来聚合邻居的社会影响力。此外,我们引入了用户明确的主题兴趣,并通过权衡每个主题的激活来捕捉基于上下文的个人兴趣。此外,我们构建了两个包含丰富用户信息的真实世界数据集。在两个数据集上进行的大量实验表明,我们的模型优于几种最先进的模型。
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Toward community answer selection by jointly static and dynamic user expertise modeling
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.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
期刊最新文献
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