Extracting representative information on Intra-organizational bogging platforms
Extracting representative information on Intra-organizational bogging platforms
复制标题
提取组织内博客平台的代表性信息
作者：
Guo Xunhua;Wei Qiang;Chen Guoqing;Zhang Jin;Qiao D;an
组织内部博客平台上生成的内容可以帮助管理者了解公司的新想法、问题和机会，而困难在于如何超越信息过载并获得整体视图。本文提出了一个从组织内博客平台中抽取代表性信息的系统框架，以及作为抽取系统核心组件的REPSET（REPresentative SET）方法。REPSET采用了一种新颖的聚类技术，旨在识别一小部分项目，这些项目在很大程度上代表了一个巨大信息库的多样化内容。基于REPSET，一个提取系统使管理者能够找到代表性的文章，这些文章可以作为全面了解员工中的热门话题，流行思想和新兴观点的起点。实证评估是基于一家大型电信公司的内部博客平台上积累的大量数据库进行的。数据实验和用户评价的结果表明，REPSET和它所基于的提取系统可以提供出色的性能，与基准方法相比。
Content generated on intra-organizational blogging platforms may help managers understand the emerging ideas, issues, and opportunities of their companies, whereas the difficulty is how to go beyond the overload of information and obtain an overall view. This paper proposes a system framework for extracting representative information from intra-organizational blogging platforms, as well as the REPSET (REPresentative SET) method, which serves as the core component of the extraction system. Drawing from a novel clustering technique, REPSET is designed to identify a small set of items that largely represent the diversified content of a huge information base. Building on REPSET, an extraction system enables managers to locate representative articles that may serve as starting points for comprehensively understanding the hot topics, prevailing thoughts, and emerging opinions among employees. Empirical evaluations are conducted based on the massive database accumulated on an internal blogging platform at a large telecommunications company. The results from data experiments and user evaluations demonstrate that REPSET and the extraction system upon which it is based can provide outstanding performance, in comparison with benchmark methods.