@inproceedings{jung2004collaborative,
  abstract = {This paper proposes the collaborative web browsing system sharing knowledge with other users. We have specifically focused on user interests extracted from bookmarks. A simple URL based-bookmark is provided with structural information by the conceptualization of the ontology. Furthermore, ontology learning based on a hierarchical clustering method can be applied to handle dynamic changes in bookmarks. As a result of our experiments, with respect to recall, about 53.1% of the total time was saved during collaborative browsing for seeking the equivalent set of information, as compared with single web browsing.},
  author = {Jung, Jason J. and Yu, Young-Hoon and Jo, GeunSik},
  booktitle = {International Conference on Computational Science},
  file = {jung2004collaborative.pdf:jung2004collaborative.pdf:PDF},
  groups = {public},
  interhash = {e1b4ebe8a3ae831c9372c7ca4f042256},
  intrahash = {ff3838b29b4d15654173fc724dcd383a},
  lastdatemodified = {2005-08-06},
  lastname = {Jung},
  note = {bibsource: DBLP, http://dblp.uni-trier.de},
  own = {own},
  pages = {513-520},
  pdf = {jung04.pdf},
  read = {read},
  timestamp = {2007-05-25 16:05:53},
  title = {Collaborative Web Browsing Based on Ontology Learning from Bookmarks.},
  url = {springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3038{\&}spage=513},
  username = {dbenz},
  year = 2004
}

@inproceedings{jung2001collaborative,
  abstract = {A bookmark means the URL information stored for memorizing a user’s own footprints and revisiting that website. This paper regards this bookmark as one of the most meaningful information representing user preferences. An original bookmark indicating only address information is categorized for merging semantic meanings by using public web directory services. These categorized bookmarks are expressed in a hierarchical tree structure. However, most current web directory services cannot afford to normalize and manage the topic hierarchy. There are several kinds of structural incompleteness such as multiple references and heterogeneous tree structures. In order to extract user prefer-ences, this paper proposes a method for driving these problems and the influ-ence propagation methods based on Bayesian networks. Therefore, the preference maps representing users’ interests are also established as tree structures. With respect to the user clustering, an approximate tree matching method is used for mapping (overlapping) users’ preference maps. It is possible to make queries and process them efficiently according to categories. Finally, this paper is applied to implement collaborative web browsing that can guide and explore the web efficiently and adaptively.},
  author = {Jung, Jason J. and Yoon, Jeong-Seob and Jo, GeunSik},
  booktitle = {{INAP}},
  file = {jung2001collaborative.pdf:jung2001collaborative.pdf:PDF},
  interhash = {07d9d02bc04354c33757866ca2708618},
  intrahash = {c49309730dcf9d62df5cbc7dc04c216e},
  lastdatemodified = {2005-08-06},
  lastname = {Jung},
  own = {own},
  pages = {343-357},
  pdf = {jung01.pdf},
  read = {notread},
  title = {Collaborative Information Filtering by Using Categorized Bookmarks on the Web},
  url = {jung01.ps},
  year = 2001
}