Maurizio Ferrari Dacrema

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Indice H	14	14
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Paolo CremonesiDept. Computer Engineering, Politecnico di MilanoEmail verificata su polimi.it
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayEmail verificata su aau.at
Fernando Benjamín Pérez MaureraPh.D. in Information Technology, DEIB, Politecnico di MilanoEmail verificata su polimi.it
Nicola FerroFull Professor, University of Padua, ItalyEmail verificata su dei.unipd.it
Nicolò FelicioniPhD student, Politecnico di MilanoEmail verificata su polimi.it
Riccardo NembriniPhD Student, DEIB, Politecnico di MilanoEmail verificata su polimi.it
Cesare BernardisOracle LabsEmail verificata su oracle.com
Yashar DeldjooTenure-Track Assistant Professor (RtD-b) of Computer Science, Polytechnic University of Bari, ItalyEmail verificata su acm.org
Markus SchedlProfessor at Johannes Kepler University Linz, Institute of Computational PerceptionEmail verificata su jku.at
Stefano CeredaPolitecnico di MilanoEmail verificata su polimi.it
Costantino CarugnoPolitecnico di MilanoEmail verificata su polimi.it
Gloria TuratiPolitecnico di MilanoEmail verificata su polimi.it
guglielmo faggioliDepartment of Information Engineering, University of PadovaEmail verificata su unipd.it
Alberto GasparinApplied Scientist, AmazonEmail verificata su amazon.de
Davide Yi Xian HuPh.D. student, Politecnico di MilanoEmail verificata su mail.polimi.it
Iván CantadorAssociate Professor of Computer Science, Universidad Autónoma de Madrid, SpainEmail verificata su uam.es
Shlomo BerkovskyMacquarie UniversityEmail verificata su mq.edu.au
Riccardo PelliniPhD Student, Politecnico di MilanoEmail verificata su polimi.it
Marcello RestelliAssociate Professor, Politecnico di MilanoEmail verificata su polimi.it
Andrea PisaniNational Ph.D. Student, Polytechnic University of Turin, Politecnico di MilanoEmail verificata su polito.it

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Maurizio Ferrari Dacrema

Assistant Professor, DEIB, Politecnico di Milano

Email verificata su polimi.it - Home page

machine learning recommender systems information retrieval quantum machine learning adiabatic quantum computation


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	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	775	2019
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	245	2021
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	120	2019
Feature Selection for Recommender Systems with Quantum Computing R Nembrini, M Ferrari Dacrema, P Cremonesi Entropy 23 (8), 970, 2021	51	2021
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	43	2022
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	39	2022
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	31	2020
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	30	2018
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	23	2020
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	22	2021
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	21	2021
Feature Selection for Classification with QAOA G Turati, MF Dacrema, P Cremonesi 2022 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2022	20	2022
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	15	2019
Towards the Evaluation of Recommender Systems with Impressions FB Perez Maurera, M Ferrari Dacrema, P Cremonesi Proceedings of the 16th ACM Conference on Recommender Systems, 610-615, 2022	14	2022
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	14	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	13*	2020
Towards Recommender Systems with Community Detection and Quantum Computing R Nembrini, C Carugno, M Ferrari Dacrema, P Cremonesi Proceedings of the 16th ACM Conference on Recommender Systems, 579-585, 2022	12	2022
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels M Ferrari Dacrema, N Felicioni, P Cremonesi Frontiers in Big Data 5, 1-21, 2022	12*	2022
Design and Evaluation of Cross-domain Recommender Systems M Ferrari Dacrema, I Cantador, I Fernández-Tobıas, S Berkovsky, ... Recommender systems handbook, 2022	12*	2022
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	12	2019

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