Discovery of User Communities from Web Audience Measurement Data
Discovery of User Communities from Web Audience Measurement Data
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DOI：
10.1109/wi.2004.56
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发表时间：
2003-09
期刊：
影响因子：
--
通讯作者：
T. Murata
中科院分区：
文献类型：
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作者：
T. Murata
在Web结构挖掘的研究中，人们对发现相关的Web页面组（Web社区）进行了一些尝试，如Kumar的拖网和Flake的方法。存在观看这些相关网页的用户组，并且发现这样的组（用户社区）对于澄清具有相似品味的用户的行为是重要的。此外，人们期望网络中用户社区的特征与真实的人类社会中的特征相对应。本文描述了一种发现用户社区的方法。客户端级日志数据（Web受众测量数据）被用作用户的Web观看行为的数据。在不分析网页内容的情况下，从日志数据得到的图中搜索最大完全二部图。实验结果表明，我们的方法成功地发现了许多有趣的用户社区的标签，表征社区。
As the research of Web structure mining, several attempts have been made for discovering group of related Web pages (Web communities) such as Kumar's trawling and Flake's method. There are groups of users who watch such related Web pages, and discovering such groups (user communities) is important for clarifying the behaviors of the users of similar tastes. Moreover, it is expected that the characteristics of user communities in the Web correspond to that in real human societies. A method for discovering user communities is described in this paper. Client-level log data (Web audience measurement data) is used as the data of users' Web watching behaviors. Maximal complete bipartite graphs are searched from the graph obtained from the log data without analyzing the contents of Web pages. Experimental results show that our method succeeds in discovering many interesting user communities with labels that characterize the communities.