| A novel graph-based model for hybrid recommendations in cold-start scenarios C Bernardis, MF Dacrema, P Cremonesi arXiv preprint arXiv:1808.10664, 2018 | 11 | 2018 |
| Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank E D'Amico, G Gabbolini, D Montesi, M Moreschini, F Parroni, F Piccinini, ... Proceedings of the Workshop on ACM Recommender Systems Challenge, 1-5, 2019 | 7 | 2019 |
| Estimating confidence of individual user predictions in item-based recommender systems C Bernardis, M Ferrari Dacrema, P Cremonesi Proceedings of the 27th ACM Conference on User Modeling, Adaptation and …, 2019 | 5 | 2019 |
| Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction N Felicioni, A Donati, L Conterio, L Bartoccioni, DYX Hu, C Bernardis, ... Proceedings of the Recommender Systems Challenge 2020, 29-33, 2020 | 3 | 2020 |
| On the instability of embeddings for recommender systems: the case of Matrix Factorization G Gabbolini, E D'Amico, C Bernardis, P Cremonesi Proceedings of the 36th Annual ACM Symposium on Applied Computing, 1363-1370, 2021 | 1 | 2021 |
| NFC: a deep and hybrid item-based model for item cold-start recommendation C Bernardis, P Cremonesi User Modeling and User-Adapted Interaction, 1-34, 2021 | | 2021 |
| Eigenvalue Perturbation for Item-based Recommender Systems C Bernardis, P Cremonesi Fifteenth ACM Conference on Recommender Systems, 656-660, 2021 | | 2021 |
Maurizio Ferrari DacremaPostdoctoral researcher, DEIB, Politecnico di MilanoEmail verificata su polimi.it
