| 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 | 155 | 2016 |
| 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 | 96 | 2017 |
| 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 | 63 | 2010 |
| Causal embeddings for recommendation S Bonner, F Vasile Proceedings of the 12th ACM conference on recommender systems, 104-112, 2018 | 44 | 2018 |
| 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 | 34 | 2009 |
| 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 | 30 | 2018 |
| Toward controlling discrimination in online ad auctions E Celis, A Mehrotra, N Vishnoi International Conference on Machine Learning, 4456-4465, 2019 | 18* | 2019 |
| 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 | 16 | 2017 |
| TRIPPER: Rule learning using taxonomies F Vasile, A Silvescu, DK Kang, V Honavar Pacific-Asia Conference on Knowledge Discovery and Data Mining, 55-59, 2006 | 14 | 2006 |
| Cost-sensitive learning for utility optimization in online advertising auctions F Vasile, D Lefortier, O Chapelle Proceedings of the ADKDD'17, 1-6, 2017 | 9 | 2017 |
| Cost-sensitive learning for bidding in online advertising auctions F Vasile, D Lefortier CoRR arXiv 1603, 2016 | 7 | 2016 |
| 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 | 5 | 2020 |
| RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. ArXiv e-prints (Aug D Rohde, S Bonner, T Dunlop, F Vasile, A Karatzoglou arXiv preprint arXiv:1808.00720, 2018 | 5 | 2018 |
| 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 | 4 | 2020 |
| REVEAL 2018: offline evaluation for recommender systems T Joachims, A Swaminathan, Y Raimond, O Koch, F Vasile Proceedings of the 12th ACM Conference on Recommender Systems, 514-515, 2018 | 4 | 2018 |
| 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 | 3 | 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 | 3 | 2019 |
| On the Value of Bandit Feedback for Offline Recommender System Evaluation O Jeunen, D Rohde, F Vasile arXiv preprint arXiv:1907.12384, 2019 | 3 | 2019 |
| Three Methods for Training on Bandit Feedback D Mykhaylov, D Rohde, F Vasile, M Bompaire, O Jeunen arXiv preprint arXiv:1904.10799, 2019 | 3 | 2019 |
| Relaxed softmax for PU learning U Tanielian, F Vasile Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019 | 2 | 2019 |
