A Scalable, Collaborative Similarity Measure for Social Annotation Systems
B. Markines, and F. Menczer. HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (July 2009)
Abstract
Collaborative annotation tools are in widespread use. The metadata from these systems can be mined to induce semantic relationships among Web objects (sites, pages, tags, concepts, users),
which in turn can support improved search, recommendation, and other Web applications. We build upon prior work by extracting relationships among tags and among resources from
two social bookmarking systems, Bibsonomy.org and GiveALink.org.
We introduce a scalable and collaborative
measure that we name maximum information path (MIP) similarity.
Our analysis shows that MIP outperforms the best scalable similarity measures in the literature. We are currently integrating MIP similarity into a number of applications under development in the GiveALink project, including search and recommendation, Web navigation maps, bookmark management, social networks, spam detection, and a tagging game to create incentives for collaborative annotations.
%0 Conference Paper
%1 markines2009scalable
%A Markines, Benjamin
%A Menczer, Filippo
%B HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia
%C New York, NY, USA
%D 2009
%I ACM
%K 2.0 folksonomy ht2009 information maximum path poster pp146 tags urls web
%T A Scalable, Collaborative Similarity Measure for Social Annotation Systems
%X Collaborative annotation tools are in widespread use. The metadata from these systems can be mined to induce semantic relationships among Web objects (sites, pages, tags, concepts, users),
which in turn can support improved search, recommendation, and other Web applications. We build upon prior work by extracting relationships among tags and among resources from
two social bookmarking systems, Bibsonomy.org and GiveALink.org.
We introduce a scalable and collaborative
measure that we name maximum information path (MIP) similarity.
Our analysis shows that MIP outperforms the best scalable similarity measures in the literature. We are currently integrating MIP similarity into a number of applications under development in the GiveALink project, including search and recommendation, Web navigation maps, bookmark management, social networks, spam detection, and a tagging game to create incentives for collaborative annotations.
@inproceedings{markines2009scalable,
abstract = {Collaborative annotation tools are in widespread use. The metadata from these systems can be mined to induce semantic relationships among Web objects (sites, pages, tags, concepts, users),
which in turn can support improved search, recommendation, and other Web applications. We build upon prior work by extracting relationships among tags and among resources from
two social bookmarking systems, Bibsonomy.org and GiveALink.org.
We introduce a scalable and collaborative
measure that we name maximum information path (MIP) similarity.
Our analysis shows that MIP outperforms the best scalable similarity measures in the literature. We are currently integrating MIP similarity into a number of applications under development in the GiveALink project, including search and recommendation, Web navigation maps, bookmark management, social networks, spam detection, and a tagging game to create incentives for collaborative annotations. },
added-at = {2009-06-16T15:00:02.000+0200},
address = {New York, NY, USA},
author = {Markines, Benjamin and Menczer, Filippo},
biburl = {https://www.bibsonomy.org/bibtex/299bc900a9ab80da8882b23b4c39c5dcc/ht09},
booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
interhash = {ad13d07b64d1bf54543546934ca4bf81},
intrahash = {99bc900a9ab80da8882b23b4c39c5dcc},
keywords = {2.0 folksonomy ht2009 information maximum path poster pp146 tags urls web},
month = {July},
paperid = {pp146},
publisher = {ACM},
session = {Poster},
timestamp = {2009-06-16T15:00:06.000+0200},
title = {A Scalable, Collaborative Similarity Measure for Social Annotation Systems},
year = 2009
}