| Meta-prod2vec: Product embeddings using side-information for recommendation F Vasile, E Smirnova, A Conneau Proceedings of the 10th ACM conference on recommender systems, 225-232, 2016 | 303 | 2016 |
| Causal embeddings for recommendation S Bonner, F Vasile Proceedings of the 12th ACM conference on recommender systems, 104-112, 2018 | 265 | 2018 |
| Contextual sequence modeling for recommendation with recurrent neural networks E Smirnova, F Vasile Proceedings of the 2nd workshop on deep learning for recommender systems, 2-9, 2017 | 184 | 2017 |
| Recogym: A reinforcement learning environment for the problem of product recommendation in online advertising D Rohde, S Bonner, T Dunlop, F Vasile, A Karatzoglou arXiv preprint arXiv:1808.00720, 2018 | 163 | 2018 |
| Resolving surface forms to wikipedia topics Y Zhou, L Nie, O Rouhani-Kalleh, F Vasile, S Gaffney Proceedings of the 23rd International Conference on Computational …, 2010 | 67 | 2010 |
| Distributionally robust counterfactual risk minimization L Faury, U Tanielian, E Dohmatob, E Smirnova, F Vasile Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3850-3857, 2020 | 50 | 2020 |
| Learning a named entity tagger from gazetteers with the partial perceptron. A Carlson, S Gaffney, F Vasile AAAI Spring Symposium: Learning by Reading and Learning to Read, 7-13, 2009 | 47 | 2009 |
| BLOB: A probabilistic model for recommendation that combines organic and bandit signals O Sakhi, S Bonner, D Rohde, F Vasile Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 38 | 2020 |
| Joint policy-value learning for recommendation O Jeunen, D Rohde, F Vasile, M Bompaire Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 32 | 2020 |
| Specializing joint representations for the task of product recommendation T Nedelec, E Smirnova, F Vasile Proceedings of the 2nd workshop on deep learning for recommender systems, 10-18, 2017 | 26 | 2017 |
| Cost-sensitive learning for utility optimization in online advertising auctions F Vasile, D Lefortier, O Chapelle Proceedings of the ADKDD'17, 1-6, 2017 | 21 | 2017 |
| On the value of bandit feedback for offline recommender system evaluation O Jeunen, D Rohde, F Vasile arXiv preprint arXiv:1907.12384, 2019 | 17 | 2019 |
| TRIPPER: Rule learning using taxonomies F Vasile, A Silvescu, DK Kang, V Honavar Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia …, 2006 | 16 | 2006 |
| A gentle introduction to recommendation as counterfactual policy learning F Vasile, D Rohde, O Jeunen, A Benhalloum Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020 | 15 | 2020 |
| Learning from Bandit Feedback: An Overview of the State-of-the-art O Jeunen, D Mykhaylov, D Rohde, F Vasile, A Gilotte, M Bompaire arXiv preprint arXiv:1909.08471, 2019 | 15 | 2019 |
| Relaxed softmax for PU learning U Tanielian, F Vasile Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019 | 12 | 2019 |
| Recommendation system-based upper confidence bound for online advertising N Nguyen-Thanh, D Marinca, K Khawam, D Rohde, F Vasile, ES Lohan, ... arXiv preprint arXiv:1909.04190, 2019 | 12 | 2019 |
| Improving offline contextual bandits with distributional robustness O Sakhi, L Faury, F Vasile arXiv preprint arXiv:2011.06835, 2020 | 10 | 2020 |
| Three methods for training on bandit feedback D Mykhaylov, D Rohde, F Vasile, M Bompaire, O Jeunen arXiv preprint arXiv:1904.10799, 2019 | 10 | 2019 |
| Siamese cookie embedding networks for cross-device user matching U Tanielian, AM Tousch, F Vasile Companion Proceedings of the The Web Conference 2018, 85-86, 2018 | 10 | 2018 |
