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.

@inproceedings{doerfel2013analysis, author = {Doerfel, Stephan and Jäschke, Robert}, title = {An Analysis of Tag-Recommender Evaluation Procedures}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, series = {RecSys '13}, publisher = {ACM}, address = {New York, NY, USA}, year = {2013}, pages = {343--346}, url = {http://doi.acm.org/10.1145/2507157.2507222}, doi = {10.1145/2507157.2507222}, isbn = {978-1-4503-2409-0}, keywords = {2013, BibSonomy, core, evaluation, iteg, itegpub, l3s, myown, recsys, tag}, 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.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert %B = Proceedings of the 7th ACM conference on Recommender systems %C = New York, NY, USA %D = 2013 %I = ACM %T = An Analysis of Tag-Recommender Evaluation Procedures %U = http://doi.acm.org/10.1145/2507157.2507222

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.

@inproceedings{doerfel2013analysis, author = {Doerfel, Stephan and Jäschke, Robert}, title = {An analysis of tag-recommender evaluation procedures}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, series = {RecSys '13}, publisher = {ACM}, address = {New York, NY, USA}, year = {2013}, pages = {343--346}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf}, doi = {10.1145/2507157.2507222}, isbn = {978-1-4503-2409-0}, keywords = {2013, bibsonomy, bookmarking, collaborative, core, evaluation, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging}, 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.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert %B = Proceedings of the 7th ACM conference on Recommender systems %C = New York, NY, USA %D = 2013 %I = ACM %T = An analysis of tag-recommender evaluation procedures %U = https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.

@inproceedings{mitzlaff2010community, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment using Evidence Networks}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, address = {Barcelona, Spain}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, keywords = {2010, assessment, bibsonomy, community, evaluation, evidence, itegpub, l3s, myown, networks}, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010) %C = Barcelona, Spain %D = 2010 %T = Community Assessment using Evidence Networks %U = http://www.kde.cs.uni-kassel.de/ws/muse2010

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.

@inproceedings{mitzlaff2010community, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment using Evidence Networks}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, address = {Barcelona, Spain}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, keywords = {2010, assessment, bibsonomy, community, evaluation, evidence, itegpub, l3s, myown, networks}, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010) %C = Barcelona, Spain %D = 2010 %T = Community Assessment using Evidence Networks %U = http://www.kde.cs.uni-kassel.de/ws/muse2010