%0 Conference Paper %1 citeulike:3500798 %A Abel, Fabian %A Henze, Nicola %A Krause, Daniel %B Proceedings of the 17th ACM Conference on Information and Knowledge Management %C New York, NY, USA %D 2008 %I ACM %K ranking social-search social-web %P 1429--1430 %R 10.1145/1458082.1458316 %T Ranking in Folksonomy Systems: Can Context Help? %U http://dx.doi.org/10.1145/1458082.1458316 %X Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems. %@ 978-1-59593-991-3

@inproceedings{citeulike:3500798, abstract = {{Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Abel, Fabian and Henze, Nicola and Krause, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/22d99518d9c1994f1a5487d6106640902/aho}, booktitle = {Proceedings of the 17th ACM Conference on Information and Knowledge Management}, citeulike-article-id = {3500798}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1458082.1458316}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1458082.1458316}, doi = {10.1145/1458082.1458316}, interhash = {5d6db50409eef97339b135ab8f703538}, intrahash = {2d99518d9c1994f1a5487d6106640902}, isbn = {978-1-59593-991-3}, keywords = {ranking social-search social-web}, location = {Napa Valley, California, USA}, pages = {1429--1430}, posted-at = {2016-06-16 21:50:49}, priority = {2}, publisher = {ACM}, series = {CIKM '08}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Ranking in Folksonomy Systems: Can Context Help?}}, url = {http://dx.doi.org/10.1145/1458082.1458316}, year = 2008 }