| BPR: Bayesian personalized ranking from implicit feedback S Rendle, C Freudenthaler, Z Gantner, L Schmidt-Thieme arXiv preprint arXiv:1205.2618, 2012 | 5509 | 2012 |
| Factorization machines S Rendle 2010 IEEE International conference on data mining, 995-1000, 2010 | 2774 | 2010 |
| Factorizing personalized markov chains for next-basket recommendation S Rendle, C Freudenthaler, L Schmidt-Thieme Proceedings of the 19th international conference on World wide web, 811-820, 2010 | 1886 | 2010 |
| Advances in collaborative filtering Y Koren, S Rendle, R Bell Recommender systems handbook, 91-142, 2021 | 1680 | 2021 |
| Factorization machines with libfm S Rendle ACM Transactions on Intelligent Systems and Technology (TIST) 3 (3), 1-22, 2012 | 1436 | 2012 |
| Pairwise interaction tensor factorization for personalized tag recommendation S Rendle, L Schmidt-Thieme Proceedings of the third ACM international conference on Web search and data …, 2010 | 833 | 2010 |
| Fast context-aware recommendations with factorization machines S Rendle, Z Gantner, C Freudenthaler, L Schmidt-Thieme Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011 | 665 | 2011 |
| MyMediaLite: A free recommender system library Z Gantner, S Rendle, C Freudenthaler, L Schmidt-Thieme Proceedings of the fifth ACM conference on Recommender systems, 305-308, 2011 | 502 | 2011 |
| Learning optimal ranking with tensor factorization for tag recommendation S Rendle, L Balby Marinho, A Nanopoulos, L Schmidt-Thieme Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 463 | 2009 |
| Improving pairwise learning for item recommendation from implicit feedback S Rendle, C Freudenthaler Proceedings of the 7th ACM international conference on Web search and data …, 2014 | 368 | 2014 |
| Learning attribute-to-feature mappings for cold-start recommendations Z Gantner, L Drumond, C Freudenthaler, S Rendle, L Schmidt-Thieme 2010 IEEE International Conference on Data Mining, 176-185, 2010 | 365 | 2010 |
| Online-updating regularized kernel matrix factorization models for large-scale recommender systems S Rendle, L Schmidt-Thieme Proceedings of the 2008 ACM conference on Recommender systems, 251-258, 2008 | 302 | 2008 |
| Neural collaborative filtering vs. matrix factorization revisited S Rendle, W Krichene, L Zhang, J Anderson Proceedings of the 14th ACM Conference on Recommender Systems, 240-248, 2020 | 279 | 2020 |
| On sampled metrics for item recommendation W Krichene, S Rendle Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 260 | 2020 |
| A generic coordinate descent framework for learning from implicit feedback I Bayer, X He, B Kanagal, S Rendle Proceedings of the 26th International Conference on World Wide Web, 1341-1350, 2017 | 221 | 2017 |
| Scaling factorization machines to relational data S Rendle Proceedings of the VLDB Endowment 6 (5), 337-348, 2013 | 128 | 2013 |
| Factorization models for context-/time-aware movie recommendations Z Gantner, S Rendle, L Schmidt-Thieme Proceedings of the workshop on context-aware movie recommendation, 14-19, 2010 | 111 | 2010 |
| On the difficulty of evaluating baselines: A study on recommender systems S Rendle, L Zhang, Y Koren arXiv preprint arXiv:1905.01395, 2019 | 110 | 2019 |
| Bpr: Bayesian personalized ranking from implicit feedback. UAI’09 S Rendle, C Freudenthaler, Z Gantner, ST Lars Arlington, Virginia, United States, 452-461, 2009 | 68 | 2009 |
| Predicting RDF triples in incomplete knowledge bases with tensor factorization L Drumond, S Rendle, L Schmidt-Thieme Proceedings of the 27th Annual ACM Symposium on Applied Computing, 326-331, 2012 | 66 | 2012 |
