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Maurizio Ferrari Dacrema
Maurizio Ferrari Dacrema
Postdoctoral researcher, DEIB, Politecnico di Milano
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα polimi.it - Αρχική σελίδα
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Παρατίθεται από
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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
4812019
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, 2021
1172021
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
772019
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
242018
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
202020
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels
N Felicioni, M Ferrari Dacrema, P Cremonesi
Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation …, 2021
142021
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
142020
Feature Selection for Recommender Systems with Quantum Computing
R Nembrini, M Ferrari Dacrema, P Cremonesi
Entropy 23 (8), 970, 2021
122021
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
102019
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
102018
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
82020
Optimizing the Selection of Recommendation Carousels with Quantum Computing
M Ferrari Dacrema, N Felicioni, P Cremonesi
Fifteenth ACM Conference on Recommender Systems (RecSys ’21), 2021
72021
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
7*2018
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
M Ferrari Dacrema, F Moroni, R Nembrini, N Ferro, G Faggioli, ...
Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022
62022
Methodological Issues in Recommender Systems Research
M Ferrari Dacrema, P Cremonesi, D Jannach
Proceedings of the Twenty-Ninth International Joint Conference on Artificial …, 2020
6*2020
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
62019
Evaluating the job shop scheduling problem on a D-wave quantum annealer
C Carugno, M Ferrari Dacrema, P Cremonesi
Nature Scientific Reports 12 (1), 2022
5*2022
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
42019
Design and Evaluation of Cross-domain Recommender Systems
M Ferrari Dacrema, I Cantador, I Fernández-Tobıas, S Berkovsky, ...
Recommender systems handbook, 2022
32022
Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract)
M Ferrari Dacrema
Proceedings of The Thirty-Fifth AAAI Conference on Artificial Intelligence …, 2021
3*2021
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