Discovery of Shared Topics Networks among People - A Simple Approach to Find Community Knowledge from WWW Bookmarks
Discovery of Shared Topics Networks among People - A Simple Approach to Find Community Knowledge from WWW Bookmarks
复制标题
发现人们之间的共享主题网络 - 从 WWW 书签中查找社区知识的简单方法
DOI：
10.1007/3-540-44533-1_67
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Yuichiro Taniguchi
中科院分区：
文献类型：
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作者：
Hideaki Takeda;Takeshi Matsuzuka;Yuichiro Taniguchi
在本文中，我们提出了一个系统称为kMedia，可以帮助用户形成知识的社区，通过显示共享的主题网络（ESTA）之间。了解他人感兴趣的话题以及自己与他人话题之间的关系是了解对方的一个重要方面。kMedia可以使用一种简单但有效的方法来找到它们。它以WWW书签中的文件夹作为感兴趣的主题，通过评价文件夹下的WWW页面的相似度来计算它们之间的关系。结果以两种方式显示。一种是通过共享主题网络来展示用户之间的关系，即，用户通过她/他的主题和与她/他的主题相关的另一个主题连接到另一个。用户可以知道她/他与其他人可以具有什么样的关系，并且更精确地知道她/他的主题对于其他人的对应物是什么。另一种方法是显示用户书签中的推荐页面。推荐页面是从其他用户的书签中选取的，它是按内容进行页面间相似性评价的主要结果。用户可以使用此结果作为她/他的书签页面的推荐，或者检查她/他的书签页面与其他页面的相关性。我们在一个实验中测试了这个系统与实际的书签数据。发现用户之间的相关主题被评价为足够好，尽管不好的结果推荐的页面。这一结果表明，我们的方法来发现用户之间的共同话题是有效的和实用的。
In this paper, we propose a system calledkMediathat can assist users to form knowledge for community by showing shared topics networks (STN) among them. One of the important aspects to know each other is to know topics interested by others and relationship between her/his and others’ topics. kMedia can use a simple but effective way to find them. It uses folders in WWW bookmarks as interested topics and can calculate their relations by evaluating similarity of WWW pages under folders. The results are displayed in two ways. One is to show relationship among users by shared topics networks, i.e., a user is connected to the other through both her/his topics and the other’s topics that are related to her/his ones. A user can know what kind of relations to others s/he can have, and more precisely know what are counterpart of her/his topics for others. The other way is to show recommended pages for pages in users’ bookmarks. Recommended pages are selected from others’ bookmarks, and it is the primary result of similarity evaluation among pages by contents. A user can use this result just as recommendation for her/his bookmarked pages or use checking how her/his bookmarked pages are related to others. We tested this system in an experiment with actual bookmark data. Discovery of related topics among users are evaluated as good enough in spite of bad results for recommendation of pages. This result tells that our approach to find common topics among users is effective and practical.