People: Christoph Freudenthaler, Mag.

Projects:

• Multi-relational Factorization Models: since April 2011
• MyMedia: April 2009 - December 2010

Teaching:

• Winter Term 2011/2012:
Image Analysis

• Summer Term 2011:
Analytical Methods (IEMS)

• Winter Term 2010/2011:
Machine Learning

• Summer Term 2010:
Analytical Methods (IEMS)

• Winter Term 2009/2010:
Artificial Intelligence

• Summer Term 2009:
Analytical Methods (IEMS)
Business Intelligence

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.
• 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.
• 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.
• 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 .
• 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 .
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Christian Wartena, Wout Slakhorst, Martin Wibbels, Zeno Gantner, Christoph Freudenthaler, Chris Newell, Lars Schmidt-Thieme (2011):
  Keyword-Based TV Program Recommendation, 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).
• 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.
• 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.
• 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.
• 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).
• Andreas Quatember, Christoph Freudenthaler (2007):
  Ein Vergleich randomisierter Antworttechniken bei Ziehen ohne Zurücklegen, Austrian Journal of Statistics 3:163-178.