基于用户行为的开源社区协同知识创新模式挖掘研究

Jun Wang, Hongde Liu, Yani Wang, Xinyu Liang
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引用次数: 0

摘要

协同知识创新活动有利于知识经济的快速发展。然而,由于协同知识创新往往隐藏在复杂的网络信息传递过程中,可能会极大地影响知识创新行为的效率和质量。以协同创新参与者、协同创新团队和协同创新成果为构成要素,构建了协同知识创新模式挖掘的研究框架。然后,利用Apriori算法挖掘协同知识创新模式,得到协同知识的转化模式。结果表明,当项目的主要贡献者为高活跃用户时,项目呈现高创新活跃趋势的概率更大,更有可能成为高质量项目。研究结果将有助于提高网络社区平台的协同知识创新能力和知识扩散效率。
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Research on collaborative knowledge innovation mode mining based on user behavior in open source community
Collaborative knowledge innovation activities are conducive to the rapid development of knowledge economy. However, because the collaborative knowledge innovation is usually hidden in the complex network information transmission process, the efficiency and quality of knowledge innovation behaviors may be greatly affected. We take collaborative innovation participants, collaborative innovation teams and collaborative innovation achievements as the constituent elements and construct the research framework of collaborative knowledge innovation mode mining. Then, we use Apriori algorithm to mine the collaborative knowledge innovation mode and obtain the transformation mode. The results show that when the main contributors to a project are high active users, the project has a greater probability of showing a trend of high innovation activity and is more likely to become a high-quality project. The findings will help to improve the collaborative knowledge innovation ability of online community platforms and the efficiency of knowledge diffusion.
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