Hideaki Takeda's Publication
- H. Takeda, T. Matsuzuka and Y. Taniguchi: Discovery
of Shared Topics Networks among People --- A Simple Approach to Find
Community Knowledge from WWW Bookmarks ---, in Proceedings of the
Pacific Rim International Conference of Artificial Intelligence (PRICAI 00),
Lecture Notes in Artificial Intelligence, No. 1886, pp. 668–678 (2000).
In this paper, we propose a system called kMedia that 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.
Hideaki Takeda (National Institute of Informatics)