BibSonomy :: BibTeX ::

tag user group author concept BibTeX key search:all search:ewomant

A blue social bookmark and publication sharing system.

entry of ewomant and 6 other people: spam

Supporting collaborative hierarchical classification: Bookmarks as an example

by: Dominik Benz and Karen H. L. Tso and Lars Schmidt-Thieme

In: Comput. Networks , Vol. 51 , Nr. 16New York, NY, USA: Elsevier North-Holland, Inc. (2007) , p. 4574--4585.

Resources (URL, PDF, PS...)

URL:	http://portal.acm.org/citation.cfm?id=1290825&coll=Portal&dl=GUIDE&CFID=46454031&CFTOKEN=27530397

Bookmarks or favorites, hotlists are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the ''classification'' of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.

Description

Supporting collaborative hierarchical classification

BibTeX record

@article{1290825,
  address = {New York, NY, USA},
  author = {Dominik Benz and Karen H. L. Tso and Lars Schmidt-Thieme},
  interHash = {181404c6a55baf6fe5db8448ac0d5bf0},
  intraHash = {ced5849c3ca3d519dd6a0ada13ee49bd},
  journal = {Comput. Networks},
  number = {16},
  pages = {4574--4585},
  publisher = {Elsevier North-Holland, Inc.},
  title = {Supporting collaborative hierarchical classification: Bookmarks as an example},
  url = {http://portal.acm.org/citation.cfm?id=1290825&coll=Portal&dl=GUIDE&CFID=46454031&CFTOKEN=27530397},
  volume = {51},
  year = {2007},
  issn = {1389-1286},
  doi = {http://dx.doi.org/10.1016/j.comnet.2007.06.014},
  abstract = {Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the ''classification'' of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.}
}

Endnote record

%0 Journal Article
%1 1290825
%A Dominik Benz
%A Karen H. L. Tso
%A Lars Schmidt-Thieme
%C New York, NY, USA
%D 2007
%I Elsevier North-Holland, Inc.
%J Comput. Networks
%K 
%N 16
%P 4574--4585
%T Supporting collaborative hierarchical classification: Bookmarks as an example
%R http://dx.doi.org/10.1016/j.comnet.2007.06.014
%U http://portal.acm.org/citation.cfm?id=1290825&coll=Portal&dl=GUIDE&CFID=46454031&CFTOKEN=27530397
%V 51
%X Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the ''classification'' of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.