融合知识特征与协同属性的创新用户群发现研究

doi:10.3772/j.issn.1000-0135.2021.05.010

情报学报

2021, Vol. 40

Issue (5): 534-546 DOI: 10.3772/j.issn.1000-0135.2021.05.010

情报用户与行为研究

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融合知识特征与协同属性的创新用户群发现研究

唐洪婷¹, 李志宏², 张沙清^1,3

1.广东工业大学，广州 510520
2.华南理工大学，广州 510641
3.惠州市广工大物联网协同创新研究院有限公司，惠州 516025

Discovering the Innovation User Groups by Converging Knowledge Characteristics and Collaborative Attributes

Tang Hongting¹, Li Zhihong², Zhang Shaqing^1,3

1.Guangdong University of Technology, Guangzhou 510520
2.South China University of Technology, Guangzhou 510641
3.Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co., Ltd., Huizhou 516025

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摘要企业创新社区作为用户参与的典型应用，可以为企业的产品开发与改进提供创新灵感。然而，互联网平台的开放性使得社区中的用户及其生成内容趋于无组织性，进而导致社区的知识组织与创新孕育效能低下。特别地，独立用户的创新活动常常受到其认知的限制。为了促使企业创新社区的用户创意得以有效聚集和碰撞，本文聚焦具体知识情景，对最具知识潜能与合作潜能的协同创新用户群进行识别，以最大限度的激发用户间的协同效用。因此，本文充分利用超网络的复杂系统分析能力，提出用户的知识特征与用户间协同属性的量化方法，并利用遗传算法，实现创新用户群发现的最优化求解。本文利用MIUI社区的真实数据进行实验研究，验证了该方法的可行性与有效性。本文着眼于创新用户群体的识别，完善了知识量化体系，同时，对在线协同创新等社区运营实践具有较好的指导意义。

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	唐洪婷
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关键词 ：知识特征, 协同创新, 用户发现, 企业创新社区, 超网络

收稿日期: 2020-05-19

基金资助:国家自然科学基金面上项目“基于超网络的大众协同创新社区用户知识主体挖掘方法研究”（71571073）；广东省软科学研究项目“大众协同创新平台的动力机制及其发展对策研究”（2020A1010020038）；广东省自然科学基金面上项目“基于区块链的大众创新社区知识共享治理机制研究”（2019A1515011370）；惠州市高校科研专项资金项目“惠州市物联网信息技术实验室”（HSJS201807）。

作者简介: 唐洪婷，女，1991年生，博士，讲师，主要研究领域为知识挖掘、社交媒体分析；李志宏，男，1969年生，博士，教授，博士生导师，主要研究领域为知识管理、知识创新，E-mail：bmzhhli@scut.edu.cn；张沙清，男，1973年生，博士，副教授，硕士生导师，主要研究领域为数据挖掘、机器学；

引用本文:

唐洪婷, 李志宏, 张沙清. 融合知识特征与协同属性的创新用户群发现研究[J]. 情报学报, 2021, 40(5): 534-546.
Tang Hongting, Li Zhihong, Zhang Shaqing. Discovering the Innovation User Groups by Converging Knowledge Characteristics and Collaborative Attributes. 情报学报, 2021, 40(5): 534-546.

链接本文:

http://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2021.05.010 或 http://qbxb.istic.ac.cn/CN/Y2021/V40/I5/534

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