| 2024 | AISTATS | Private Learning with Public Features. | Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang |
| 2024 | CIKM | Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling. | Anushya Subbiah, Steffen Rendle, Vikram Aggarwal |
| 2022 | RecSys | Revisiting the Performance of iALS on Item Recommendation Benchmarks. | Steffen Rendle, Walid Krichene, Li Zhang, Yehuda Koren |
| 2021 | ICML | Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. | Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang |
| 2021 | IJCAI | On Sampled Metrics for Item Recommendation (Extended Abstract). | Walid Krichene, Steffen Rendle |
| 2020 | CIKM | Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval. | Tao Wu, Ellie Ka In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John R. Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao |
| 2020 | KDD | On Sampled Metrics for Item Recommendation. | Walid Krichene, Steffen Rendle |
| 2020 | RecSys | Neural Collaborative Filtering vs. Matrix Factorization Revisited. | Steffen Rendle, Walid Krichene, Li Zhang, John R. Anderson |
| 2019 | ICLR | Efficient Training on Very Large Corpora via Gramian Estimation. | Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson |
| 2018 | ICML | Adaptive Sampled Softmax with Kernel Based Sampling. | Guy Blanc, Steffen Rendle |
| 2017 | WWW | A Generic Coordinate Descent Framework for Learning from Implicit Feedback. | Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle |
| 2016 | KDD | Robust Large-Scale Machine Learning in the Cloud. | Steffen Rendle, Dennis Fetterly, Eugene J. Shekita, Bor-Yiing Su |
| 2015 | PAKDD | Graph Based Relational Features for Collective Classification. | Immanuel Bayer, Uwe Nagel, Steffen Rendle |
| 2014 | WSDM | Improving pairwise learning for item recommendation from implicit feedback. | Steffen Rendle, Christoph Freudenthaler |
| 2013 | RecSys | Sample selection for MCMC-based recommender systems. | Thierry Silbermann, Immanuel Bayer, Steffen Rendle |
| 2012 | SAC | Predicting RDF triples in incomplete knowledge bases with tensor factorization. | Lucas Drumond, Steffen Rendle, Lars Schmidt-Thieme |
| 2012 | WSDM | Learning recommender systems with adaptive regularization. | Steffen Rendle |
| 2011 | GI | Network effects on interest rates in online social lending. | Ulrik Brandes, Jrgen Lerner, Bobo Nick, Steffen Rendle |
| 2011 | RecSys | MyMediaLite: a free recommender system library. | Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2011 | SIGIR | Fast context-aware recommendations with factorization machines. | Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2010 | ICDM | Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. | Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme |
| 2010 | ICDM | Factorization Machines. | Steffen Rendle |
| 2010 | WWW | Factorizing personalized Markov chains for next-basket recommendation. | Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme |
| 2010 | WSDM | Pairwise interaction tensor factorization for personalized tag recommendation. | Steffen Rendle, Lars Schmidt-Thieme |
| 2009 | KDD | Learning optimal ranking with tensor factorization for tag recommendation. | Steffen Rendle, Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme |
| 2009 | PAKDD | Learning to Extract Relations for Relational Classification. | Steffen Rendle, Christine Preisach, Lars Schmidt-Thieme |
| 2009 | UAI | BPR: Bayesian Personalized Ranking from Implicit Feedback. | Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme |
| 2008 | ICDM | Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference. | Steffen Rendle, Lars Schmidt-Thieme |
| 2008 | PAKDD | Scaling Record Linkage to Non-uniform Distributed Class Sizes. | Steffen Rendle, Lars Schmidt-Thieme |
| 2008 | RecSys | Online-updating regularized kernel matrix factorization models for large-scale recommender systems. | Steffen Rendle, Lars Schmidt-Thieme |
| 2006 | ICDM | Object Identification with Constraints. | Steffen Rendle, Lars Schmidt-Thieme |