Maurizio Ferrari Dacrema
Maurizio Ferrari Dacrema
Postdoctoral researcher, DEIB, Politecnico di Milano
Email verificata su polimi.it - Home page
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Citata da
Citata da
Anno
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
M Ferrari Dacrema, P Cremonesi, D Jannach
Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019), 2019
146*2019
Movie Genome: Alleviating New Item Cold Start in Movie Recommendation
Y Deldjoo, M Ferrari Dacrema, M Gabriel Constantin, H Eghbal-Zadeh, ...
User Modeling and User-Adapted Interaction (UMUAI), 2019
272019
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario
S Antenucci, S Boglio, E Chioso, E Dervishaj, S Kang, T Scarlatti, ...
Proceedings of the ACM Recommender Systems Challenge 2018, 4, 2018
192018
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
M Ferrari Dacrema, S Boglio, P Cremonesi, D Jannach
ACM Transactions on Information Systems, 2019
10*2019
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
52019
A novel graph-based model for hybrid recommendations in cold-start scenarios
C Bernardis, M Ferrari Dacrema, P Cremonesi
Proceedings of the Late-Breaking Results track part of the Twelfth ACM …, 2018
42018
Deriving item features relevance from collaborative domain knowledge
M Ferrari Dacrema, A Gasparin, P Cremonesi
Proceedings of KaRS 2018 Workshop on Knowledge-aware and Conversational …, 2018
32018
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features
LL Costanzo, Y Deldjoo, MF Dacrema, M Schedl, P Cremonesi
IntRS workshop, The 13th ACM Conference on Recommender Systems (RecSys …, 2019
22019
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
12019
Eigenvalue analogy for confidence estimation in item-based recommender systems
M Ferrari Dacrema, P Cremonesi
Proceedings of the Late-Breaking Results track part of the Twelfth ACM …, 2018
1*2018
User Preference Sources: Explicit vs. Implicit Feedback
P Cremonesi, F Garzotto, M Ferrari Dacrema
Collaborative Recommendations, 233-252, 2018
12018
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
M Ferrari Dacrema, F Parroni, P Cremonesi, D Jannach
Proceedings of the 29th ACM International Conference on Information and …, 2020
2020
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
2020
ContentWise Impressions: An industrial dataset with impressions included
FB Pérez Maurera, M Ferrari Dacrema, L Saule, M Scriminaci, ...
Proceedings of the 29th ACM International Conference on Information and …, 2020
2020
Methodological Issues in Recommender Systems Research
M Ferrari Dacrema, P Cremonesi, D Jannach
Proceedings of the Twenty-Ninth International Joint Conference on Artificial …, 2020
2020
On the effectiveness of convolutional neural networks in modelling latent dimensions interactions for recommender systems
F PARRONI
Italy, 2019
2019
Rating aware feature selection in content-based recommender systems
A Cano
Italy, 2019
2019
Eigenvalues as confidence estimators in item based recommender systems
S ANTENUCCI, E CHIOSO
Italy, 2019
2019
Automated feature engineering for recommender systems
I INAJJAR, G LOCCI
Italy, 2018
2018
Aggregating Models for Anomaly Detection in Space Systems: Results from the FCTMAS Study
F Amigoni, M Ferrari Dacrema, A Donati, C Laroque, M Lavagna, A Riva
International Conference on Intelligent Autonomous Systems, 142-160, 2018
2018
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20