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People: Christoph Freudenthaler, Mag.
Office hour:
C208 Spl, upon request
Phone, Fax, Email:
Phone: +49 5121/ 883-856
Fax: +49 5121 / 883-859
e-mail:
Postal address:
Information Systems and Machine Learning Lab
Universitätsplatz 1
University of Hildesheim
31141 Hildesheim
Germany
Visitor address:
Information Systems and Machine Learning Lab
Samelsonplatz 1
University of Hildesheim
31141 Hildesheim
Germany

Projects:

Teaching:

Bachelor/Master Thesis:

  • Summer Term 2011: Christian Brauch: Season-aware Bayesian Probabilistic Ranking: Time-aware Matrix Factorization for Item Prediction

Publications:
Books:

  • Christoph Freudenthaler (2008):
    Statistische Methoden und Modelle fuer die moderne Zukunftsbank als Kreditfabrik, VDM-Verlag.
Articles:
  • Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme (2015):
    Comparing Prediction Models for Active Learning in Recommender systems, in Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), Trier, Germany. PDF
  • Lucas Drumond, Lars Schmidt-Thieme, Christoph Freudenthaler, Artus Krohn-Grimberghe (2014):
    Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification , in Advances in Knowledge Discovery and Data Mining - 18th Pacific-Asia Conference, (PAKDD 2014) , Tainan, Taiwan. PDF
  • Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme (2012):
    Exploiting the Characteristics of Matrix Factorization for Active Learning in Recommender Systems, in Doctoral Symposium of the 6th Annual ACM Conference on Recommender Systems (RecSys), Dublin, Irelan, pp. 317-320. PDF
  • Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme (2012):
    Personalized Ranking for Non-Uniformly Sampled Items, Journal of Machine Learning Research Workshop and Conference Proceedings . PDF
  • Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme (2012):
    Multi-Relational Matrix Factorization using Bayesian Personalized Ranking for Social Network Data , Proceedings of the Fifth ACM International Conference on Web Search and Data Mining . PDF
  • Christoph Freudenthaler, Lars Schmidt-Thieme, Steffen Rendle (2011):
    Bayesian Factorization Machines, in Workshop on Sparse Representation and Low-rank Approximation, Neural Information Processing Systems (NIPS), Granada, Spain. PDF
  • Christoph Freudenthaler, Lars Schmidt-Thieme, Steffen Rendle (2011):
    Factorization Machines - Factorized Polynomial Regression Models , in Proceedings of the 2nd German Polish Symposium on Data Analysis and Its Applications (GPSDAA), Cracow, Poland. PDF
  • Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme (2011):
    Factorizing Markov Models for Categorical Time Series Prediction , in American Institute of Physics (AIP) Conference Proceedings of the 9th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), Halkidiki, Greece. PDF
  • Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme (2011):
    Towards Optimal Active Learning for Matrix Factorization in Recommender Systems, in 23th IEEE International Conference on Tools With Artificial Intelligence (ICTAI), Florida, USA. PDF
  • Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme (2011):
    Non-myopic Active Learning for Recommender Systems based on Matrix Factorization, in 12th IEEE International Conference on Information Reuse and Integration (IRI), Las Vegas, USA. PDF
  • Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme (2011):
    Bayesian Personalized Ranking for Non-Uniformly Sampled Items, in KDD Cup Workshop 2011, San Diego, USA. PDF
  • Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme (2011):
    MyMediaLite: A Free Recommender System Library, in 5th ACM International Conference on Recommender Systems (RecSys 2011), Chicago, USA. PDF
  • Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme (2011):
    Fast Context-aware Recommendations with Factorization Machines, in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), Beijing, China. PDF
  • Christian Wartena, Wout Slakhorst, Martin Wibbels, Zeno Gantner, Christoph Freudenthaler, Chris Newell, Lars Schmidt-Thieme (2011):
    Keyword-Based TV Program Recommendation, to appear in 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP'11), Barcelona, Spain.
  • Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme (2011):
    Active Learning for Aspect Model in Recommender Systems, in IEEE Symposium on Computational Intelligence and Data Mining (CIDM). PDF
  • Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme (2010):
    Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia. PDF
  • Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme (2010):
    Factorizing Personalized Markov Chains for Next-Basket Recommendation, in Proceedings of the 19th International World Wide Web Conference (WWW 2010), ACM. Best Paper Award. PDF
  • Zeno Gantner, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme (2009):
    Optimal Ranking for Video Recommendation, in User Centric Media: First International Conference, UCMedia 2009, Revised Selected Papers, Springer. PDF
  • Paul Marrow, Rich Hanbidge, Steffen Rendle, Christian Wartena, Christoph Freudenthaler (2009):
    MyMedia: Producing an Extensible Framework for Recommendation, in Networked Electronic Media Summit 2009.
  • Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme (2009):
    BPR: Bayesian Personalized Ranking from Implicit Feedback, in Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). PDF
  • Andreas Quatember, Christoph Freudenthaler (2007):
    Ein Vergleich randomisierter Antworttechniken bei Ziehen ohne Zurücklegen, Austrian Journal of Statistics 3:163-178.