A Method of Discovery of Shared Topic Networks among People from WWW Bookmarks and Its Evaluations
A Method of Discovery of Shared Topic Networks among People from WWW Bookmarks and Its Evaluations
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一种从WWW书签中发现人们共享主题网络的方法及其评价
DOI：
10.1527/tjsai.17.276
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发表时间：
2002
影响因子：
--
通讯作者：
M. Kidode
中科院分区：
文献类型：
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作者：
Masahiro Hamasaki;Hideaki Takeda;Takeshi Matsuzuka;Yuichiro Taniguchi;Y. Kono;M. Kidode
在本文中，我们提出共享主题网络作为人类网络的模型来组织互联网信息，并开发了一个名为 kMedia 的系统，该系统可以使用 WWW 书签文件生成共享主题网络。我们还通过实验评估该系统，以了解共享主题网络如何帮助用户尤其是彼此了解。共享主题网络是通过连接参与者的主题而形成的，用于了解他人的兴趣并与他人交换信息。 kMedia 可以利用 WWW 书签的结构生成共享主题网络，即书签的文件夹被视为其所有者的主题。通过聚合这些主题中的页面之间的相似性来估计不同用户的主题之间的关系。进行实验是为了澄清两点：一是话题是否是人与人之间交流信息的更好方式，二是我们如何衡量人际关系。第一点考察主题推荐比页面推荐更容易被接受。对于第二点，我们提出类别相似度作为人际关系的衡量标准。由于我们比较了主题属于同一社区的案例和不属于社区的案例之间的结果，我们注意到主题结构的相似性是情感上的。类别相似度是对主题结构的相似性进行估计，并被证明在人际关系的测量方面比任何其他参数都更好。
In this paper, we propose shared topic networks as a model of human network to organize Internet Information, and developed a system called kMedia that can generate shared topic networks by using WWW bookmark files. We also evaluate the system with experiments to know how shared topics network can help uesrs especially to know each other. A shared topic network is formed by linking topics of participants, and used to know interests of others and to exchange information with others. kMedia can generate shared topics networks by using structures of WWW bookmarks, i.e., folders of bookmarks are regarded as topics of their owners. Relations among topics of different users are estimated by aggregating similarity among pages in these topics. The experiments were performed to clarify two points; one is whether topics is a better way to exchange information among people and the other is how we can measure human relationship. The first point is examined that topic recommendation is more acceptable than page recommendation. For the second point, we propose category resemblance as measurement of human relationship. Since we compare results between cases with subjects belonging to the same community and cases without communities, we noticed similarity of topics structure is affective. The category resemblance is to estimate this similarity of topic structure and it is proved that it is better than any other parameters with respect to measurement for human relationship.