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, Band 1120, Seiten 7-24.
CEUR-WS, Aachen, Germany, 2014.
Folke Mitzlaff, Stephan Doerfel, Andreas Hotho, Robert Jäschke und Juergen Mueller.
[doi]
[Kurzfassung]
[BibTeX]
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.
Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles.
In:
Modeling and Mining Ubiquitous Social Media.
Springer Verlag, Heidelberg, Germany, 2012.
Martin Atzmueller, Stephan Doerfel, Andreas Hotho, Folke Mitzlaff und Gerd Stumme.
[BibTeX]
Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players.
In:
Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation.
2011.
Martin Atzmueller, Stephan Doerfel, Andreas Hotho, Folke Mitzlaff und Gerd Stumme.
[BibTeX]
Factor Models for Tag Recommendation in BibSonomy.
In: F. Eisterlehner, A. Hotho und R. Jäschke
(Herausgeber):
ECML PKDD Discovery Challenge 2009 (DC09), Band 497, Seiten 235-242.
CEUR Workshop Proceedings, Bled, Slovenia, 2009.
Steffen Rendle und Lars Schmidt-Thieme.
[doi]
[Kurzfassung]
[BibTeX]
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.
BibSonomy: A Social Bookmark and Publication Sharing System.
In: A. de Moor, S. Polovina und H. Delugach
(Herausgeber):
Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, Seiten 87-102.
Aalborg Universitetsforlag, Aalborg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.