TY  - CONF
AU  - Jäschke, Robert
AU  - Eisterlehner, Folke
AU  - Hotho, Andreas
AU  - Stumme, Gerd
A2  - 
T1  - Testing and Evaluating Tag Recommenders in a Live System
T2  - RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems
PB  - ACM
C1  - New York, NY, USA
PY  - 2009/
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - 
DO  - 
KW  - 2009
KW  - BibSonomy
KW  - conference
KW  - folksonomy
KW  - framework
KW  - recommender
L1  - 
SN  - 
N1  - tag-recommender für acm09
N1  - 
AB  - 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.
ER  -

TY  - CONF
AU  - Rendle, Steffen
AU  - Schmidt-Thieme, Lars
A2  - Eisterlehner, Folke
A2  - Hotho, Andreas
A2  - Jäschke, Robert
T1  - Factor Models for Tag Recommendation in BibSonomy
T2  - ECML PKDD Discovery Challenge 2009 (DC09)
PB  - CEUR Workshop Proceedings
C1  - Bled, Slovenia
PY  - 2009/10
CY  -  
VL  - 497
IS  - 
SP  - 235
EP  - 242
UR  - http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/
DO  - 
KW  - 3d
KW  - folksonomy
KW  - kde
KW  - recommender
KW  - social
KW  - tensor
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 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.
ER  -