%0 Conference Paper %1 Heymann:2008:STP:1390334.1390425 %A Heymann, Paul %A Ramage, Daniel %A Garcia-Molina, Hector %B Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval %C New York, NY, USA %D 2008 %I ACM %K association_rules heymann social_tag_prediction social_tags %P 531--538 %R 10.1145/1390334.1390425 %T Social Tag Prediction %U http://doi.acm.org/10.1145/1390334.1390425 %X In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems. %@ 978-1-60558-164-4

@inproceedings{Heymann:2008:STP:1390334.1390425, abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.}, acmid = {1390425}, added-at = {2017-05-14T05:13:48.000+0200}, address = {New York, NY, USA}, author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector}, biburl = {https://www.bibsonomy.org/bibtex/203a3f5239616e5fec8d1fe53cfdf270c/hangdong}, booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {10.1145/1390334.1390425}, interhash = {bb9455c80cc9bd8cf95c951a1318dabc}, intrahash = {03a3f5239616e5fec8d1fe53cfdf270c}, isbn = {978-1-60558-164-4}, keywords = {association_rules heymann social_tag_prediction social_tags}, location = {Singapore, Singapore}, numpages = {8}, pages = {531--538}, publisher = {ACM}, series = {SIGIR '08}, timestamp = {2017-05-14T05:13:48.000+0200}, title = {Social Tag Prediction}, url = {http://doi.acm.org/10.1145/1390334.1390425}, year = 2008 }