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Knowledge Sharing in Peer-to-Peer Online Communities: The Effects of Recommendation Agents and Community Characteristics

机译:对等在线社区中的知识共享:推荐代理的影响和社区特征

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Knowledge sharing (KS) is generally performed in a centralized fashion, through a knowledge repository. However, the centralized knowledge management systems may sometimes cause problems in sharing knowledge. One potential solution is to conduct KS in a decentralized network supported by peer-to-peer technology. While extensive research has been done to address the flaws of current reputation-based feedback mechanisms for promoting knowledge diffusion in peer-to-peer communities, some serious drawbacks of feedback mechanisms, namely dimensionality and interaction-dependency effects, have been overlooked. Using an agent-based simulation, this paper examines these effects while also considering characteristics of the community. Results of our simulation show interesting findings, for example, that negligence of knowledge dimensionality and interaction-dependency in designing reputation mechanisms has a major negative effect on community knowledge performance. Further, more complex knowledge adversely affects community-level diffusion outcomes whereas greater network density improves them.
机译:知识共享(KS)通常是通过知识库以集中方式执行的。但是,集中式知识管理系统有时可能会在共享知识方面引起问题。一种潜在的解决方案是在对等技术支持的分散网络中进行KS。尽管已经进行了广泛的研究来解决当前基于信誉的反馈机制在促进对等社区中知识传播方面的缺陷,但反馈机制的一些严重缺陷,即维度和交互依赖效应,却被忽略了。使用基于代理的模拟,本文研究了这些影响,同时还考虑了社区的特征。我们的模拟结果显示出有趣的发现,例如,在设计信誉机制时,知识维度和交互依赖的疏忽会对社区知识绩效产生重大负面影响。此外,更复杂的知识会对社区级别的传播结果产生不利影响,而更大的网络密度则可以改善它们。

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