The Social Web is successfully established and poised for continued growth. Web
2.0 applications such as blogs, bookmarking, music, photo and video sharing
systems are among the most popular; and all of them incorporate a social aspect,
i.e., users can easily share information with other users. But due to the
diversity of these applications -- serving different aims -- the Social Web is
ironically divided. Blog users who write about music for example, could possibly
benefit from other users registered in other social systems operating within the
same domain, such as a social radio station. Although these sites are two
different and disconnected systems, offering distinct services to the users, the
fact that domains are compatible could benefit users from both systems with
interesting and multi-faceted information. In this paper we propose to
automatically establish social links between distinct social systems through
cross-tagging, i.e., enriching a social system with the tags of other similar
social system(s). Since tags are known for increasing the prediction quality of
recommender systems (RS), we propose to quantitatively evaluate the extent to
which users can benefit from cross-tagging by measuring the impact of different
cross-tagging approaches on tag-aware RS for personalized
resource recommendations. We conduct experiments in real world data sets and
empirically show the effectiveness of our approaches.
%0 Conference Paper
%1 Stewart09
%A Stewart, Avaré
%A Diaz-Aviles, Ernesto
%A Marinho, Leandro Balby
%A Nanopoulos, Alexandros
%A Schmidt-Thieme, Lars
%A Nejdl, Wolfgang
%B Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (HT '09)
%C New York, NY, USA
%D 2009
%I ACM
%K CrossTagging SocialSearch collaborative-filtering myown
%P 271--278
%T Cross-Tagging for Personalized Open Social Networking
%U http://dl.acm.org/citation.cfm?id=1557960
%X The Social Web is successfully established and poised for continued growth. Web
2.0 applications such as blogs, bookmarking, music, photo and video sharing
systems are among the most popular; and all of them incorporate a social aspect,
i.e., users can easily share information with other users. But due to the
diversity of these applications -- serving different aims -- the Social Web is
ironically divided. Blog users who write about music for example, could possibly
benefit from other users registered in other social systems operating within the
same domain, such as a social radio station. Although these sites are two
different and disconnected systems, offering distinct services to the users, the
fact that domains are compatible could benefit users from both systems with
interesting and multi-faceted information. In this paper we propose to
automatically establish social links between distinct social systems through
cross-tagging, i.e., enriching a social system with the tags of other similar
social system(s). Since tags are known for increasing the prediction quality of
recommender systems (RS), we propose to quantitatively evaluate the extent to
which users can benefit from cross-tagging by measuring the impact of different
cross-tagging approaches on tag-aware RS for personalized
resource recommendations. We conduct experiments in real world data sets and
empirically show the effectiveness of our approaches.
@inproceedings{Stewart09,
abstract = {The Social Web is successfully established and poised for continued growth. Web
2.0 applications such as blogs, bookmarking, music, photo and video sharing
systems are among the most popular; and all of them incorporate a social aspect,
i.e., users can easily share information with other users. But due to the
diversity of these applications -- serving different aims -- the Social Web is
ironically divided. Blog users who write about music for example, could possibly
benefit from other users registered in other social systems operating within the
same domain, such as a social radio station. Although these sites are two
different and disconnected systems, offering distinct services to the users, the
fact that domains are compatible could benefit users from both systems with
interesting and multi-faceted information. In this paper we propose to
automatically establish social links between distinct social systems through
cross-tagging, i.e., enriching a social system with the tags of other similar
social system(s). Since tags are known for increasing the prediction quality of
recommender systems (RS), we propose to quantitatively evaluate the extent to
which users can benefit from cross-tagging by measuring the impact of different
cross-tagging approaches on tag-aware RS for personalized
resource recommendations. We conduct experiments in real world data sets and
empirically show the effectiveness of our approaches.
},
added-at = {2011-01-04T14:22:10.000+0100},
address = {New York, NY, USA},
author = {Stewart, Avaré and Diaz-Aviles, Ernesto and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Nejdl, Wolfgang},
biburl = {https://www.bibsonomy.org/bibtex/2881a67f23c5cf4812ef0faaa678c3d47/lbalby},
booktitle = {Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (HT '09)},
interhash = {72c406ffac43c380feeb38d26fec73de},
intrahash = {881a67f23c5cf4812ef0faaa678c3d47},
keywords = {CrossTagging SocialSearch collaborative-filtering myown},
month = {July},
pages = {271--278},
paperid = {fp081},
publisher = {ACM},
session = {Full Paper},
timestamp = {2014-11-02T20:57:14.000+0100},
title = {Cross-Tagging for Personalized Open Social Networking},
url = {http://dl.acm.org/citation.cfm?id=1557960},
year = 2009
}