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Christoph Freudenthaler
Researcher at University of Hildesheim
Publications - 24
Citations - 8373
Christoph Freudenthaler is an academic researcher from University of Hildesheim. The author has contributed to research in topics: Recommender system & Active learning (machine learning). The author has an hindex of 14, co-authored 24 publications receiving 6463 citations. Previous affiliations of Christoph Freudenthaler include Institute of Food and Agricultural Sciences & University of Konstanz.
Papers
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Proceedings Article
BPR: Bayesian personalized ranking from implicit feedback
TL;DR: In this article, the authors proposed a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem, which is based on stochastic gradient descent with bootstrap sampling.
Proceedings ArticleDOI
Factorizing personalized Markov chains for next-basket recommendation
TL;DR: This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Posted Content
BPR: Bayesian Personalized Ranking from Implicit Feedback
TL;DR: This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
Proceedings ArticleDOI
Fast context-aware recommendations with factorization machines
TL;DR: This work proposes to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions and shows empirically that this approach outperforms Multiverse Recommendation in prediction quality and runtime.
Proceedings ArticleDOI
MyMediaLite: a free recommender system library
TL;DR: The library addresses two common scenarios in collaborative filtering: rating prediction and item prediction from positive-only implicit feedback, and contains methods for real-time updates and loading/storing of already trained recommender models.