Jäschke, R.; Eisterlehner, F.; Hotho, A. & Stumme, G.: Testing and Evaluating Tag Recommenders in a Live System. RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2009
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.
@inproceedings{jaeschke2009testing,
author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
title = {Testing and Evaluating Tag Recommenders in a Live System},
booktitle = {RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems},
publisher = {ACM},
address = {New York, NY, USA},
year = {2009},
note = {(to appear)},
keywords = {2009, BibSonomy, conference, folksonomy, framework, recommender},
abstract = {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.}
}
Rendle, S. & Schmidt-Thieme, L.: Factor Models for Tag Recommendation in BibSonomy. In: Eisterlehner, F.; Hotho, A. & Jäschke, R. (Hrsg.): ECML PKDD Discovery Challenge 2009 (DC09). Bled, Slovenia: CEUR Workshop Proceedings, 2009 (497), S. 235-242
[Volltext]
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.
@inproceedings{marinho:ecml2009,
author = {Rendle, Steffen and Schmidt-Thieme, Lars},
title = {Factor Models for Tag Recommendation in BibSonomy},
editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)},
publisher = {CEUR Workshop Proceedings},
address = {Bled, Slovenia},
year = {2009},
volume = {497},
pages = {235--242},
url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/},
keywords = {3d, folksonomy, kde, recommender, social, tensor},
abstract = {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.}
}