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 abel2009contextbased
%A Abel, Fabian
%A Baldoni, Matteo
%A Baroglio, Cristina
%A Henze, Nicola
%A Krause, Daniel
%A Patti, Viviana
%B HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia
%C New York, NY, USA
%D 2009
%I ACM
%K adaptation context folksonomies fp060 fullPaper ht2009 media ranking search social
%T Context-based Ranking in Folksonomies
%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.
@inproceedings{abel2009contextbased,
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 = {2009-06-16T15:00:02.000+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/217d5c35426963e20875ec1dc42913855/ht09},
booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
interhash = {0e0dff0c21fd77d2d1f0224317c4974f},
intrahash = {17d5c35426963e20875ec1dc42913855},
keywords = {adaptation context folksonomies fp060 fullPaper ht2009 media ranking search social},
month = {July},
paperid = {fp060},
publisher = {ACM},
session = {Full Paper},
timestamp = {2009-06-16T15:00:04.000+0200},
title = {Context-based Ranking in Folksonomies},
year = 2009
}