@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}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, doi = {10.1145/2507157.2507222}, interhash = {3eaf2beb1cdad39b7c5735a82c3338dd}, intrahash = {a73213a865503252caa4b28e88a77108}, isbn = {978-1-4503-2409-0}, location = {Hong Kong, China}, numpages = {4}, pages = {343--346}, publisher = {ACM}, series = {RecSys '13}, title = {An Analysis of Tag-Recommender Evaluation Procedures}, url = {http://doi.acm.org/10.1145/2507157.2507222}, year = 2013 } @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}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, doi = {10.1145/2507157.2507222}, interhash = {3eaf2beb1cdad39b7c5735a82c3338dd}, intrahash = {a73213a865503252caa4b28e88a77108}, isbn = {978-1-4503-2409-0}, location = {Hong Kong, China}, numpages = {4}, pages = {343--346}, publisher = {ACM}, series = {RecSys '13}, title = {An Analysis of Tag-Recommender Evaluation Procedures}, url = {http://doi.acm.org/10.1145/2507157.2507222}, year = 2013 } @inproceedings{doerfel2012leveraging, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, acmid = {2365937}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {d3e6fa8023b173228a959914affc8d73}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, numpages = {8}, pages = {9--16}, publisher = {ACM}, series = {RSWeb '12}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation}, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @inproceedings{jaeschke2009testing, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.}, address = {New York, NY, USA}, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, booktitle = {RecSys '09: Proceedings of the third ACM Conference on Recommender Systems}, doi = {10.1145/1639714.1639790}, interhash = {440fafda1eccf4036066f457eb6674a0}, intrahash = {21fdf612ba6b356fb1b311fc9369f32d}, isbn = {978-1-60558-435-5}, location = {New York, NY, USA}, pages = {369--372}, publisher = {ACM}, title = {Testing and Evaluating Tag Recommenders in a Live System}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/jaeschke2009testing.pdf}, vgwort = {15}, year = 2009 }