| Time and activity sequence prediction of business process instances M Polato, A Sperduti, A Burattin, M de Leoni Computing 100 (9), 1005-1031, 2018 | 81 | 2018 |
| Data-aware remaining time prediction of business process instances M Polato, A Sperduti, A Burattin, M de Leoni 2014 International Joint Conference on Neural Networks (IJCNN), 816-823, 2014 | 68 | 2014 |
| LSTM networks for data-aware remaining time prediction of business process instances N Navarin, B Vincenzi, M Polato, A Sperduti 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017 | 36 | 2017 |
| Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation M Polato, F Aiolli Neurocomputing 268, 17-26, 2017 | 15 | 2017 |
| Boolean kernels for collaborative filtering in top-N item recommendation M Polato, F Aiolli Neurocomputing 286, 214-225, 2018 | 12 | 2018 |
| Radius-margin ratio optimization for dot-product boolean kernel learning I Lauriola, M Polato, F Aiolli International conference on artificial neural networks, 183-191, 2017 | 11 | 2017 |
| Kernel based collaborative filtering for very large scale top-n item recommendation M Polato, F Aiolli Proceedings of the European Symposium on Artificial Neural Networks …, 2016 | 10 | 2016 |
| Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis F Aiolli, M Conti, A Gangwal, M Polato Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 1484-1491, 2019 | 7 | 2019 |
| A preliminary study on a recommender system for the job recommendation challenge M Polato, F Aiolli Proceedings of the Recommender Systems Challenge 2016, 2016 | 7 | 2016 |
| A novel boolean kernels family for categorical data M Polato, I Lauriola, F Aiolli Entropy 20 (6), 444, 2018 | 5 | 2018 |
| The minimum effort maximum output principle applied to Multiple Kernel Learning. I Lauriola, M Polato, F Aiolli ESANN, 2018 | 4 | 2018 |
| Classification of categorical data in the feature space of monotone dnfs M Polato, I Lauriola, F Aiolli International Conference on Artificial Neural Networks, 279-286, 2017 | 4 | 2017 |
| Tag-based user profiling: a game theoretic approach G Faggioli, M Polato, F Aiolli Adjunct Publication of the 27th Conference on User Modeling, Adaptation and …, 2019 | 3 | 2019 |
| Learning with subsampled kernel-based methods: Environmental and financial applications MA Shahrokhabadi, A Neisy, E Perracchione, M Polato Dolomites Research Notes on Approximation 12 (1), 2019 | 3 | 2019 |
| Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning M Polato, F Aiolli 33rd AAAI Conference on Artificial Intelligence, 2019 | 3 | 2019 |
| Boolean kernels for rule based interpretation of support vector machines M Polato, F Aiolli Neurocomputing 342, 113-124, 2019 | 2 | 2019 |
| Efficient similarity based methods for the playlist continuation task G Faggioli, M Polato, F Aiolli Proceedings of the ACM Recommender Systems Challenge 2018, 1-6, 2018 | 2 | 2018 |
| Recency aware collaborative filtering for next basket recommendation G Faggioli, M Polato, F Aiolli Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020 | 1 | 2020 |
| Learning preferences for large scale multi-label problems I Lauriola, M Polato, A Lavelli, F Rinaldi, F Aiolli International Conference on Artificial Neural Networks, 546-555, 2018 | 1 | 2018 |
| A game-theoretic framework for interpretable preference and feature learning M Polato, F Aiolli International Conference on Artificial Neural Networks, 659-668, 2018 | 1 | 2018 |
