Paolo Cremonesi

ORCID

Publication Activity (10 Years)

Years Active: 1993-2023
Publications (10 Years): 126

Top Venues

Publications

Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro
qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. CLEF (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Characterizing Impression-Aware Recommender Systems. LERI@RecSys (2023)
Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi
Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023. SIGIR Forum 57 (2) (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. CoRR (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Incorporating Impressions to Graph-Based Recommenders. LERI@RecSys (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. QCE (2023)
Riccardo Pellini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers. IIR (2023)
Luca Benedetto, Paolo Cremonesi, Andrew Caines, Paula Buttery, Andrea Cappelli, Andrea Giussani, Roberto Turrin
A Survey on Recent Approaches to Question Difficulty Estimation from Text. ACM Comput. Surv. 55 (9) (2023)
Alessandro Barenghi, Paolo Cremonesi, Gerardo Pelosi
Quantum Computing Research Lines in the Italian Center for Supercomputing. SAMOS (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Impression-Aware Recommender Systems. CoRR (2023)
Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi
Workshop on Learning and Evaluating Recommendations with Impressions (LERI). RecSys (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Impressions in Recommender Systems: Present and Future. IIR (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Classification with QAOA. QCE (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. ECIR (1) (2022)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Evaluating Recommendations in a User Interface With Multiple Carousels. IIR (2022)
Pietro Chiavassa, Andrea Marchesin, Ignazio Pedone, Maurizio Ferrari Dacrema, Paolo Cremonesi
Virtual Network Function Embedding with Quantum Annealing. QCE (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. SIGIR (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Feature Selection via Quantum Annealers for Ranking and Classification Tasks. IIR (2022)
João Vinagre, Alípio Mário Jorge, Marie Al-Ghossein, Albert Bifet, Paolo Cremonesi
Preface to the special issue on dynamic recommender systems and user models. User Model. User Adapt. Interact. 32 (4) (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards the Evaluation of Recommender Systems with Impressions. RecSys (2022)
Ervin Dervishaj, Paolo Cremonesi
GAN-based Matrix Factorization for Recommender Systems. CoRR (2022)
Matteo Montanari, Cesare Bernardis, Paolo Cremonesi
On the impact of data sampling on hyper-parameter optimisation of recommendation algorithms. SAC (2022)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. Frontiers Big Data 5 (2022)
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) (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. CoRR (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Replication of Recommender Systems with Impressions. IIR (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. CoRR (2022)
Ervin Dervishaj, Paolo Cremonesi
GAN-based matrix factorization for recommender systems. SAC (2022)
Cesare Bernardis, Paolo Cremonesi
NFC: a deep and hybrid item-based model for item cold-start recommendation. User Model. User Adapt. Interact. 32 (4) (2022)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Classification with QAOA. CoRR (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Replication of Collaborative Filtering Generative Adversarial Networks on Recommender Systems. IIR (2022)
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 (2022)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Marcello Restelli, Paolo Cremonesi
Off-Policy Evaluation with Deficient Support Using Side Information. NeurIPS (2022)
Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards Recommender Systems with Community Detection and Quantum Computing. RecSys (2022)
Federico Rios, Paolo Rizzo, Francesco Puddu, Federico Romeo, Andrea Lentini, Giuseppe Asaro, Filippo Rescalli, Cristiana Bolchini, Paolo Cremonesi
Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach. UMAP (Adjunct Publication) (2022)
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Recommender Systems with Quantum Computing. Entropy 23 (8) (2021)
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi
On the instability of embeddings for recommender systems: the case of matrix factorization. SAC (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Fernando Benjamín Pérez Maurera, Paolo Cremonesi
Measuring the Ranking Quality of Recommendations in a Two-Dimensional Carousel Setting. IIR (2021)
Paolo Cremonesi, Dietmar Jannach
Progress in Recommender Systems Research: Crisis? What Crisis? AI Mag. 42 (3) (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. CoRR (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. UMAP (Adjunct Publication) (2021)
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Recommender Systems with Quantum Computing. CoRR (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. IMX (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. CoRR (2021)
Luca Benedetto, Giovanni Aradelli, Paolo Cremonesi, Andrea Cappelli, Andrea Giussani, Roberto Turrin
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text. BEA@EACL (2021)
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39 (2) (2021)
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi
On the instability of embeddings for recommender systems: the case of Matrix Factorization. CoRR (2021)
Ekaterina Loginova, Luca Benedetto, Dries Benoit, Paolo Cremonesi
Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text. RANLP (2021)
Stefano Cereda, Stefano Valladares, Paolo Cremonesi, Stefano Doni
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions. Proc. VLDB Endow. 14 (8) (2021)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Optimizing the Selection of Recommendation Carousels with Quantum Computing. RecSys (2021)
Cesare Bernardis, Paolo Cremonesi
Eigenvalue Perturbation for Item-based Recommender Systems. RecSys (2021)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi
ContentWise Impressions: An industrial dataset with impressions included. CoRR (2020)
Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, Stefano Doni
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations. CoRR (2020)
Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi
Introducing a framework to assess newly created questions with Natural Language Processing. CoRR (2020)
Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi
Introducing a Framework to Assess Newly Created Questions with Natural Language Processing. AIED (1) (2020)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi
ContentWise Impressions: An Industrial Dataset with Impressions Included. CIKM (2020)
Gabriele Prato, Federico Sallemi, Paolo Cremonesi, Mario Scriminaci, Stefan Gudmundsson, Silvio Palumbo
Outfit Completion and Clothes Recommendation. CHI Extended Abstracts (2020)
Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, Stefano Doni
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations. LCTES (2020)
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CoRR (2020)
Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi
R2DE: a NLP approach to estimating IRT parameters of newly generated questions. CoRR (2020)
Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi
R2DE: a NLP approach to estimating IRT parameters of newly generated questions. LAK (2020)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Methodological Issues in Recommender Systems Research (Extended Abstract). IJCAI (2020)
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CIKM (2020)
Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi
Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv. 53 (5) (2020)
Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. IntRS@RecSys (2019)
Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi
Movie genome: alleviating new item cold start in movie recommendation. User Model. User Adapt. Interact. 29 (2) (2019)
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. CoRR (2019)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys (2019)
Paolo Cremonesi
A pragmatic and industry-aware approach toward the design of on-line recommender systems. ORSUM@RecSys (2019)
Massimo Quadrana, Dietmar Jannach, Paolo Cremonesi
Tutorial: Sequence-Aware Recommender Systems. WWW (Companion Volume) (2019)
Luca Benedetto, Paolo Cremonesi, Manuel Parenti
A Virtual Teaching Assistant for Personalized Learning. CoRR (2019)
Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. CoRR (2019)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. CoRR (2019)
Luca Benedetto, Paolo Cremonesi
Rexy, A Configurable Application for Building Virtual Teaching Assistants. INTERACT (2) (2019)
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. UMAP (2019)
Massimo Quadrana, Paolo Cremonesi
Sequence-aware recommendation. RecSys (2018)
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
A novel graph-based model for hybrid recommendations in cold-start scenarios. CoRR (2018)
Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi
Content-Based Multimedia Recommendation Systems: Definition and Application Domains. IIR (2018)
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
Deriving item features relevance from collaborative domain knowledge. CoRR (2018)
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Sequence-aware Recommender Systems. UMAP (2018)
Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi
Audio-visual encoding of multimedia content for enhancing movie recommendations. RecSys (2018)
Yashar Deldjoo, Mihai Gabriel Constantin, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi
MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval. MMSys (2018)
Maurizio Ferrari Dacrema, Paolo Cremonesi
Eigenvalue analogy for confidence estimation in item-based recommender systems. CoRR (2018)
Luca Benedetto, Paolo Cremonesi, Manuel Parenti
A Virtual Teaching Assistant for Personalized Learning. CIKM Workshops (2018)
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Sequence-Aware Recommender Systems. ACM Comput. Surv. 51 (4) (2018)
Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi
Using visual features based on MPEG-7 and deep learning for movie recommendation. Int. J. Multim. Inf. Retr. 7 (4) (2018)
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
Deriving Item Features Relevance from Collaborative Domain Knowledge. KaRS@RecSys (2018)
Paolo Cremonesi, Chiara Francalanci, Alessandro Poli, Roberto Pagano, Luca Mazzoni, Alberto Maggioni, Mehdi Elahi
Social Network based Short-Term Stock Trading System. CoRR (2018)
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Sequence-Aware Recommender Systems. CoRR (2018)
Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer, Paolo Cremonesi
The Importance of Song Context in Music Playlists. RecSys Posters (2017)
Stefano Cereda, Leonardo Cella, Paolo Cremonesi
Estimate Features Relevance for Groups of Users. IIR (2017)
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. RecSys (2017)
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. CoRR (2017)
Yashar Deldjoo, Paolo Cremonesi, Markus Schedl, Massimo Quadrana
The effect of different video summarization models on the quality of video recommendation based on low-level visual features. CBMI (2017)
Paolo Cremonesi, Mehdi Elahi, Franca Garzotto
User interface patterns in recommendation-empowered content intensive multimedia applications. Multim. Tools Appl. 76 (4) (2017)
Leonardo Cella, Romaric Gaudel, Paolo Cremonesi
Kernalized Collaborative Contextual Bandits. RecSys Posters (2017)
Roberto Pagano, Massimo Quadrana, Mehdi Elahi, Paolo Cremonesi
Toward Active Learning in Cross-domain Recommender Systems. CoRR (2017)
Yashar Deldjoo, Cristina Frà, Massimo Valla, Paolo Cremonesi
Letting Users Assist What to Watch: An Interactive Query-by-Example Movie Recommendation System. IIR (2017)
Leonardo Cella, Stefano Cereda, Massimo Quadrana, Paolo Cremonesi
Deriving Item Features Relevance from Past User Interactions. UMAP (2017)
Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi, Paolo Cremonesi
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Paolo Cremonesi