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

Yuchao Liu, Meng Liu, Jianhua Yin
{"title":"Toward community answer selection by jointly static and dynamic user expertise modeling","authors":"Yuchao Liu, Meng Liu, Jianhua Yin","doi":"10.1017/ATSIP.2020.28","DOIUrl":null,"url":null,"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.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/ATSIP.2020.28","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"APSIPA Transactions on Signal and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/ATSIP.2020.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过静态和动态用户专业知识建模实现社区答案选择
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology Speech-and-Text Transformer: Exploiting Unpaired Text for End-to-End Speech Recognition GP-Net: A Lightweight Generative Convolutional Neural Network with Grasp Priority Reversible Data Hiding in Compressible Encrypted Images with Capacity Enhancement Convolutional Neural Networks Inference Memory Optimization with Receptive Field-Based Input Tiling
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1