With the advent of Web 2.0 tagging became a popular feature. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. Clicking on a tag enables the users to explore related content. In this paper we investigate how such tag-based queries, initialized by the clicking activity, can be enhanced with automatically produced contextual information so that the search result better fits to the actual aims of the user. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way.
%0 Conference Paper
%1 citeulike:5030985
%A Abel, Fabian
%A Baldoni, Matteo
%A Baroglio, Cristina
%A Henze, Nicola
%A Krause, Daniel
%A Patti, Viviana
%B HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia
%C New York, NY, USA
%D 2009
%I ACM
%K dlpaws dppaws ht09 japaws ranking tagging user-profile
%P 209--218
%R 10.1145/1557914.1557951
%T Context-based ranking in folksonomies
%U http://dx.doi.org/10.1145/1557914.1557951
%X With the advent of Web 2.0 tagging became a popular feature. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. Clicking on a tag enables the users to explore related content. In this paper we investigate how such tag-based queries, initialized by the clicking activity, can be enhanced with automatically produced contextual information so that the search result better fits to the actual aims of the user. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way.
%@ 978-1-60558-486-7
@inproceedings{citeulike:5030985,
abstract = {With the advent of Web 2.0 tagging became a popular feature. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. Clicking on a tag enables the users to explore related content. In this paper we investigate how such tag-based queries, initialized by the clicking activity, can be enhanced with automatically produced contextual information so that the search result better fits to the actual aims of the user. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way.},
added-at = {2009-07-01T11:12:30.000+0200},
address = {New York, NY, USA},
author = {Abel, Fabian and Baldoni, Matteo and Baroglio, Cristina and Henze, Nicola and Krause, Daniel and Patti, Viviana},
biburl = {https://www.bibsonomy.org/bibtex/2b6eedadfa41511731e525cfe56096406/brusilovsky},
booktitle = {HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia},
citeulike-article-id = {5030985},
doi = {10.1145/1557914.1557951},
interhash = {0e0dff0c21fd77d2d1f0224317c4974f},
intrahash = {b6eedadfa41511731e525cfe56096406},
isbn = {978-1-60558-486-7},
keywords = {dlpaws dppaws ht09 japaws ranking tagging user-profile},
location = {Torino, Italy},
pages = {209--218},
posted-at = {2009-07-01 08:44:17},
priority = {3},
publisher = {ACM},
timestamp = {2009-07-01T11:12:31.000+0200},
title = {Context-based ranking in folksonomies},
url = {http://dx.doi.org/10.1145/1557914.1557951},
year = 2009
}