面向用户需求主题的在线问答社区信息多层级分类研究

doi:10.3772/j.issn.1000-0135.2022.08.008

情报学报

2022, Vol. 41

Issue (8): 860-871 DOI: 10.3772/j.issn.1000-0135.2022.08.008

情报技术与应用

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面向用户需求主题的在线问答社区信息多层级分类研究

成全, 张燕刚

福州大学经济与管理学院，福州 350116

Information Multi-hierarchical Classification of Online Q&A Community Oriented to Users' Needs Topics

Cheng Quan, Zhang Yangang

School of Economics and Management, Fuzhou University, Fuzhou 350116

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摘要互联网在线问答社区服务的广泛性、便捷性、互动性与个性化特征促进了该模式的飞速发展，在线问答社区已逐渐成为人们获取各类生活信息的重要平台。然而，社区内信息资源存在缺乏有效组织与语义关联缺失等现实瓶颈，以及生活类信息的高复杂、多层级等特性，导致用户的在线信息需求服务体验效果不尽如人意。为实现对在线问答社区内各类信息资源的细粒度组织与语义关系揭示，进而达到面向用户需求主题实施信息精准分类的目标，本研究利用在线母婴社区内用户提问数据构建用户需求的多层级架构体系，进而生成经过验证的、具有多层级需求主题的标签化数据实验样本。最后，通过比对本研究所构建的面向用户需求主题的信息多层级分类模型（users' needs topics - hierarchical classification，UNT-HC）与TextAttBiRNN（text attention bi-directional recurrent neural network）单层级分类模型及HFT-CNN（hierarchical fine-tuning conventional neural network）、HCCNN（hierarchical classification conventional neural network）等多层级分类模型的分类效果，验证了UNT-HC模型在实现在线问答社区中多层级单标签、超细粒度文本信息分类应用中性能的优越性。

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	成全
	张燕刚

关键词 ：用户信息需求, 需求主题, 在线问答社区, 多层级分类, 机器学习

收稿日期: 2021-03-29

基金资助:国家社会科学基金一般项目“多源在线医疗健康信息的语义融合及精准推荐服务研究”（19BTQ072）。

作者简介: 成全，男，1979年生，博士，教授，硕士生导师，主要研究领域为数据挖掘与知识发现、知识管理与知识工程，E-mail：chengquan@fzu.edu.cn；张燕刚，男，1995年生，硕士研究生，主要研究领域为数据挖掘与知识发现、网络舆；

引用本文:

成全, 张燕刚. 面向用户需求主题的在线问答社区信息多层级分类研究[J]. 情报学报, 2022, 41(8): 860-871.
Cheng Quan, Zhang Yangang. Information Multi-hierarchical Classification of Online Q&A Community Oriented to Users' Needs Topics. 情报学报, 2022, 41(8): 860-871.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.08.008 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I8/860

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