Following a successful first edition, we are pleased to announce the 2nd edition of the Large Scale Hierarchical Text Classification (LSHTC) Pascal Challenge. The LSHTC Challenge is a hierarchical text classification competition, using large datasets. This year’s challenge will increase the scale and the difficulty of the task, using data from Wikipedia (www.wikipedia.org), in addition to the ODP Web directory data (www.dmoz.org).
The Natural Language Decathlon (decaNLP) is a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
he Diagnostic Competition is proposed to be the first of a series of international competitions that will be hosted yearly at the International Workshop on Principles of Diagnosis (DX).
This year's discovery challenge presents two tasks in the new area of social bookmarking. One task covers spam detection and the other covers tag recommendations. As we are hosting the social bookmark and publication sharing system BibSonomy, we are able to provide a dataset of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are invited to present their approaches and results.
This year's discovery challenge presents two tasks in the new area
of social bookmarking. One task covers spam detection and
the other covers tag recommendations. As we are hosting the social bookmark and
publication sharing system BibSonomy, we are able to provide a dataset
of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline
extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are
invited to present their approaches and results.
This year's discovery challenge presents two tasks in the new area
of social bookmarking. One task covers spam detection and
the other covers tag recommendations. As we are hosting the social bookmark and
publication sharing system BibSonomy, we are able to provide a dataset
of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline
extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are
invited to present their approaches and results.
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, und G. Stumme. Recommender Systems for the Social Web, Volume 32 von Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)