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ORCIDCesare Bernardis
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2020 – today
- 2022
[b1]Cesare Bernardis:
On the effectiveness of neighborhood-based models in recommender systems. Polytechnic University of Milan, Italy, 2022- [j3]Cesare Bernardis
, Maurizio Ferrari Dacrema, Fernando Benjamín Pérez Maurera, Massimo Quadrana, Mario Scriminaci, Paolo Cremonesi:
From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain. IEEE Access 10: 14779-14796 (2022) - [j2]Edoardo D'Amico, Giovanni Gabbolini, Cesare Bernardis, Paolo Cremonesi:
Analyzing and improving stability of matrix factorization for recommender systems. J. Intell. Inf. Syst. 58(2): 255-285 (2022) - [j1]Cesare Bernardis, Paolo Cremonesi:
NFC: a deep and hybrid item-based model for item cold-start recommendation. User Model. User Adapt. Interact. 32(4): 747-780 (2022) - [c7]Matteo Montanari, Cesare Bernardis, Paolo Cremonesi:
On the impact of data sampling on hyper-parameter optimisation of recommendation algorithms. SAC 2022: 1399-1402 - [i3]Cesare Bernardis:
Multi-stage Ensemble Model for Cross-market Recommendation. CoRR abs/2202.08824 (2022) - 2021
- [c6]Luca Carminati, Giacomo Lodigiani, Pietro Maldini, Samuele Meta, Stiven Metaj, Arcangelo Pisa, Alessandro Sanvito, Mattia Surricchio, Fernando Benjamín Pérez Maurera, Cesare Bernardis, Maurizio Ferrari Dacrema:
Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment. RecSys Challenge 2021: 28-33 - [c5]Cesare Bernardis, Paolo Cremonesi:
Eigenvalue Perturbation for Item-based Recommender Systems. RecSys 2021: 656-660 - [c4]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of matrix factorization. SAC 2021: 1363-1370 - [i2]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of Matrix Factorization. CoRR abs/2104.05796 (2021) - 2020
- [c3]Nicolò Felicioni, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, Maurizio Ferrari Dacrema:
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction. RecSys Challenge 2020: 29-33
2010 – 2019
- 2019
- [c2]Edoardo D'Amico, Giovanni Gabbolini, Daniele Montesi, Matteo Moreschini, Federico Parroni, Federico Piccinini, Alberto Rossettini, Alessio Russo Introito, Cesare Bernardis, Maurizio Ferrari Dacrema:
Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank. RecSys Challenge 2019: 7:1-7:5 - [c1]Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. UMAP 2019: 149-156 - 2018
- [i1]Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi:
A novel graph-based model for hybrid recommendations in cold-start scenarios. CoRR abs/1808.10664 (2018)
Coauthor Index
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