BPR: Bayesian personalized ranking from implicit feedback S Rendle, C Freudenthaler, Z Gantner, L Schmidt-Thieme arXiv preprint arXiv:1205.2618, 2012 | 5279 | 2012 |

Factorization machines S Rendle 2010 IEEE International conference on data mining, 995-1000, 2010 | 2657 | 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 | 1757 | 2010 |

Advances in collaborative filtering Y Koren, S Rendle, R Bell Recommender systems handbook, 91-142, 2021 | 1637 | 2021 |

Factorization machines with libfm S Rendle ACM Transactions on Intelligent Systems and Technology (TIST) 3 (3), 1-22, 2012 | 1397 | 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 | 821 | 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 | 651 | 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 | 500 | 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 | 456 | 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 | 365 | 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 | 357 | 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 | 296 | 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 | 253 | 2020 |

On sampled metrics for item recommendation W Krichene, S Rendle Communications of the ACM 65 (7), 75-83, 2022 | 229 | 2022 |

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 | 210 | 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 | 106 | 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 | 67 | 2009 |

Learning recommender systems with adaptive regularization S Rendle Proceedings of the fifth ACM international conference on Web search and data …, 2012 | 65 | 2012 |