Factor Models for Tag Recommendation in BibSonomy
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ECML PKDD Discovery Challenge 2009 (DC09), 497, page 235--242. Bled, Slovenia, CEUR Workshop Proceedings, (September 2009)

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.
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