%0 Conference Paper %1 doerfel2013analysis %A Doerfel, Stephan %A Jäschke, Robert %B Proceedings of the 7th ACM conference on Recommender systems %C New York, NY, USA %D 2013 %I ACM %K 2013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging %P 343--346 %R 10.1145/2507157.2507222 %T An analysis of tag-recommender evaluation procedures %U https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf %X Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores. %@ 978-1-4503-2409-0

@inproceedings{doerfel2013analysis, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.}, acmid = {2507222}, added-at = {2013-12-16T17:19:49.000+0100}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert}, biburl = {https://www.bibsonomy.org/bibtex/2aa4b3d79a362d7415aaa77625b590dfa/stumme}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, doi = {10.1145/2507157.2507222}, interhash = {3eaf2beb1cdad39b7c5735a82c3338dd}, intrahash = {aa4b3d79a362d7415aaa77625b590dfa}, isbn = {978-1-4503-2409-0}, keywords = {2013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging}, location = {Hong Kong, China}, numpages = {4}, pages = {343--346}, publisher = {ACM}, series = {RecSys '13}, timestamp = {2013-12-16T17:55:19.000+0100}, title = {An analysis of tag-recommender evaluation procedures}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf}, year = 2013 }