Mitzlaff, F.; Doerfel, S.; Hotho, A.; Jäschke, R. & Mueller, J. (2014), Summary of the 15th Discovery Challenge: Recommending Given Names, in '15th Discovery Challenge of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013, Prague, Czech Republic - Sctober 27, 2013. Proceedings' , CEUR-WS, Aachen, Germany , pp. 7--24 .
[BibTeX] [Endnote]

The 15th ECML PKDD Discovery Challenge centered around the recommendation of given names. Participants of the challenge implemented algorithms that were tested both offline - on data collected by the name search engine Nameling - and online within Nameling. Here, we describe both tasks in detail and discuss the publicly available datasets. We motivate and explain the chosen evaluation of the challenge, and we summarize the different approaches applied to the name recommendation tasks. Finally, we present the rankings and winners of the offline and the online phase.

Atzmueller, M.; Doerfel, S.; Hotho, A.; Mitzlaff, F. & Stumme, G. (2012), Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles 'Modeling and Mining Ubiquitous Social Media' , Springer Verlag, Heidelberg, Germany .
[BibTeX] [Endnote]

2011

Atzmueller, M.; Doerfel, S.; Hotho, A.; Mitzlaff, F. & Stumme, G. (2011), Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players, in 'Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation' .
[BibTeX] [Endnote]

2009

Rendle, S. & Schmidt-Thieme, L. (2009), Factor Models for Tag Recommendation in BibSonomy, in Folke Eisterlehner; Andreas Hotho & Robert Jäschke, ed., 'ECML PKDD Discovery Challenge 2009 (DC09)' , CEUR Workshop Proceedings, Bled, Slovenia , pp. 235--242 .
[BibTeX] [Endnote]

This paper describes our approach to the ECML/PKDD Discovery Challenge 2009. Our approach is a pure statistical model taking no content information into account. It tries to find latent interactions between users, items and tags by factorizing the observed tagging data. The factorization model is learned by the Bayesian Personal Ranking method (BPR) which is inspired by a Bayesian analysis of personalized ranking with missing data. To prevent overfitting, we ensemble the models over several iterations and hyperparameters. Finally, we enhance the top-n lists by estimating how many tags to recommend.

2006

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), BibSonomy: A Social Bookmark and Publication Sharing System, in Aldo de Moor; Simon Polovina & Harry Delugach, ed., 'Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures' , Aalborg Universitetsforlag, Aalborg , pp. 87-102 .
[BibTeX] [Endnote]

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references in a kind of personal library.