User Preference Sources: Explicit vs. Implicit Feedback. - Paolo Cremonesi; Franca Garzotto; Maurizio Ferrari Dacrema (Collaborative Recommendations, 2018)

Aggregating Models for Anomaly Detection in Space Systems: Results from the FCTMAS Study. - Francesco Amigoni; Maurizio Ferrari Dacrema; Alessandro Donati; Christian Laroque; Michèle Lavagna; Alessandro Riva (IAS, 2018)

Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario. - Sebastiano Antenucci; Simone Boglio; Emanuele Chioso; Ervin Dervishaj; Shuwen Kang; Tommaso Scarlatti; Maurizio Ferrari Dacrema (RecSys Challenge, 2018)

Deriving Item Features Relevance from Collaborative Domain Knowledge. - Maurizio Ferrari Dacrema; Alberto Gasparin; Paolo Cremonesi (KaRS@RecSys, 2018)

A novel graph-based model for hybrid recommendations in cold-start scenarios. - Cesare Bernardis; Maurizio Ferrari Dacrema; Paolo Cremonesi (CoRR, 2018)

Eigenvalue analogy for confidence estimation in item-based recommender systems. - Maurizio Ferrari Dacrema; Paolo Cremonesi (CoRR, 2018)

Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. - Luca Luciano Costanzo; Yashar Deldjoo; Maurizio Ferrari Dacrema; Markus Schedl; Paolo Cremonesi (IntRS@RecSys, 2019)

Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank. - Edoardo D'Amico; Giovanni Gabbolini; Daniele Montesi; Matteo Moreschini; Federico Parroni; Federico Piccinini; Alberto Rossettini; Alessio Russo Introito; Cesare Bernardis; Maurizio Ferrari Dacrema (RecSys Challenge, 2019)

Are we really making much progress? A worrying analysis of recent neural recommendation approaches. - Maurizio Ferrari Dacrema; Paolo Cremonesi; Dietmar Jannach (RecSys, 2019)

Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. - Cesare Bernardis; Maurizio Ferrari Dacrema; Paolo Cremonesi (UMAP, 2019)

Movie genome: alleviating new item cold start in movie recommendation. - Yashar Deldjoo; Maurizio Ferrari Dacrema; Mihai Gabriel Constantin; Hamid Eghbal-zadeh; Stefano Cereda; Markus Schedl; Bogdan Ionescu; Paolo Cremonesi (User Model. User Adapt. Interact., 2019)

Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. - Maurizio Ferrari Dacrema; Federico Parroni; Paolo Cremonesi; Dietmar Jannach (CIKM, 2020)

ContentWise Impressions: An Industrial Dataset with Impressions Included. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Lorenzo Saule; Mario Scriminaci; Paolo Cremonesi (CIKM, 2020)

Methodological Issues in Recommender Systems Research (Extended Abstract). - Maurizio Ferrari Dacrema; Paolo Cremonesi; Dietmar Jannach (IJCAI, 2020)

Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction. - Nicolò Felicioni; Andrea Donati; Luca Conterio; Luca Bartoccioni; Davide Yi Xian Hu; Cesare Bernardis; Maurizio Ferrari Dacrema (RecSys Challenge, 2020)

An assessment of reproducibility and methodological issues in neural recommender systems research. - Maurizio Ferrari Dacrema (2020)

Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract). - Maurizio Ferrari Dacrema (AAAI, 2021)

Measuring the Ranking Quality of Recommendations in a Two-Dimensional Carousel Setting. - Nicolò Felicioni; Maurizio Ferrari Dacrema; Fernando Benjamín Pérez Maurera; Paolo Cremonesi (IIR, 2021)

Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment. - 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 (RecSys Challenge, 2021)

Optimizing the Selection of Recommendation Carousels with Quantum Computing. - Maurizio Ferrari Dacrema; Nicolò Felicioni; Paolo Cremonesi (RecSys, 2021)

Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. - Nicolò Felicioni; Maurizio Ferrari Dacrema; Paolo Cremonesi (IMX, 2021)

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. - Nicolò Felicioni; Maurizio Ferrari Dacrema; Paolo Cremonesi (UMAP (Adjunct Publication), 2021)

Feature Selection for Recommender Systems with Quantum Computing. - Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (Entropy, 2021)

A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. - Maurizio Ferrari Dacrema; Simone Boglio; Paolo Cremonesi; Dietmar Jannach (ACM Trans. Inf. Syst., 2021)

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (ECIR (1), 2022)

Evaluating Recommendations in a User Interface With Multiple Carousels. - Maurizio Ferrari Dacrema; Nicolò Felicioni; Paolo Cremonesi (IIR, 2022)

Feature Selection via Quantum Annealers for Ranking and Classification Tasks. - Maurizio Ferrari Dacrema; Fabio Moroni; Riccardo Nembrini; Nicola Ferro 001; Guglielmo Faggioli; Paolo Cremonesi (IIR, 2022)

Replication of Collaborative Filtering Generative Adversarial Networks on Recommender Systems. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (IIR, 2022)

Replication of Recommender Systems with Impressions. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (IIR, 2022)

Off-Policy Evaluation with Deficient Support Using Side Information. - Nicolò Felicioni; Maurizio Ferrari Dacrema; Marcello Restelli; Paolo Cremonesi (NeurIPS, 2022)

Virtual Network Function Embedding with Quantum Annealing. - Pietro Chiavassa; Andrea Marchesin; Ignazio Pedone; Maurizio Ferrari Dacrema; Paolo Cremonesi (QCE, 2022)

Feature Selection for Classification with QAOA. - Gloria Turati; Maurizio Ferrari Dacrema; Paolo Cremonesi (QCE, 2022)

Towards the Evaluation of Recommender Systems with Impressions. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (RecSys, 2022)

Towards Recommender Systems with Community Detection and Quantum Computing. - Riccardo Nembrini; Costantino Carugno; Maurizio Ferrari Dacrema; Paolo Cremonesi (RecSys, 2022)

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. - Maurizio Ferrari Dacrema; Fabio Moroni; Riccardo Nembrini; Nicola Ferro 001; Guglielmo Faggioli; Paolo Cremonesi (SIGIR, 2022)

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain. - Cesare Bernardis; Maurizio Ferrari Dacrema; Fernando Benjamín Pérez Maurera; Massimo Quadrana; Mario Scriminaci; Paolo Cremonesi (IEEE Access, 2022)

Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. - Maurizio Ferrari Dacrema; Nicolò Felicioni; Paolo Cremonesi (Frontiers Big Data, 2022)

Design and Evaluation of Cross-Domain Recommender Systems. - Maurizio Ferrari Dacrema; Iván Cantador; Ignacio Fernández-Tobías; Shlomo Berkovsky; Paolo Cremonesi (Recommender Systems Handbook, 2022)

An Evaluation of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (Zenodo, 2022)

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Paolo Cremonesi (Zenodo, 2022)

qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. - Andrea Pasin; Maurizio Ferrari Dacrema; Paolo Cremonesi; Nicola Ferro 001 (CLEF, 2023)

Impressions in Recommender Systems: Present and Future. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Pablo Castells; Paolo Cremonesi (IIR, 2023)

Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers. - Riccardo Pellini; Maurizio Ferrari Dacrema; Paolo Cremonesi (IIR, 2023)

Characterizing Impression-Aware Recommender Systems. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Pablo Castells; Paolo Cremonesi (LERI@RecSys, 2023)

Incorporating Impressions to Graph-Based Recommenders. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Pablo Castells; Paolo Cremonesi (LERI@RecSys, 2023)

Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. - Gloria Turati; Maurizio Ferrari Dacrema; Paolo Cremonesi (QCE, 2023)

Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation. - Paolo Basso; Arturo Benedetti; Nicola Cecere; Alessandro Maranelli; Salvatore Marragony; Samuele Peri; Andrea Riboni; Alessandro Verosimile; Davide Zanutto; Maurizio Ferrari Dacrema (RecSys Challenge, 2023)

Workshop on Learning and Evaluating Recommendations with Impressions (LERI). - Maurizio Ferrari Dacrema; Pablo Castells; Justin Basilico; Paolo Cremonesi (RecSys, 2023)

Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023. - Maurizio Ferrari Dacrema; Pablo Castells; Justin Basilico; Paolo Cremonesi (SIGIR Forum, 2023)

Reinforcement Learning for Variational Quantum Circuit Design. - Simone Foderà; Gloria Turati; Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (AIQxQIA@AI*IA, 2024)

An Application of Reinforcement Learning for Minor Embedding in Quantum Annealing (Short Paper). - Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (AIQxQIA@AI*IA, 2024)

QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. - Andrea Pasin; Maurizio Ferrari Dacrema; Paolo Cremonesi; Nicola Ferro 001 (CLEF (Working Notes), 2024)

Overview of QuantumCLEF 2024: The Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. - Andrea Pasin; Maurizio Ferrari Dacrema; Paolo Cremonesi; Nicola Ferro 001 (CLEF (2), 2024)

Quantum Computing for Information Retrieval and Recommender Systems. - Maurizio Ferrari Dacrema; Andrea Pasin; Paolo Cremonesi; Nicola Ferro 001 (ECIR (5), 2024)

QuantumCLEF - Quantum Computing at CLEF. - Andrea Pasin; Maurizio Ferrari Dacrema; Paolo Cremonesi; Nicola Ferro 001 (ECIR (5), 2024)

Leveraging Semantic Embeddings of User Reviews with Off-the-Shelf LLMs for Recommender Systems. - Nicola Cecere; Andrea Pisani; Maurizio Ferrari Dacrema; Paolo Cremonesi (IIR, 2024)

Pre-Trained LLM Embeddings of Product Reviews for Recommendation. - Andrea Pisani; Nicola Cecere; Maurizio Ferrari Dacrema; Paolo Cremonesi (IIR, 2024)

Adaptive Learning for Quantum Linear Regression. - Costantino Carugno; Maurizio Ferrari Dacrema; Paolo Cremonesi (QCE, 2024)

Exploiting Contextual Normalizations and Article Endorsement for News Recommendation. - Andrea Alari; Lorenzo Campana; Federico Giuseppe Ciliberto; Saverio Maggese; Carlo Sgaravatti; Francesco Zanella; Andrea Pisani; Maurizio Ferrari Dacrema (RecSys Challenge, 2024)

Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems. - Maurizio Ferrari Dacrema; Andrea Pasin; Paolo Cremonesi; Nicola Ferro 001 (SIGIR, 2024)

AutoOPE: Automated Off-Policy Estimator Selection. - Nicolò Felicioni; Michael Benigni; Maurizio Ferrari Dacrema (CoRR, 2024)

Reinforcement Learning for Variational Quantum Circuits Design. - Simone Foderà; Gloria Turati; Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (CoRR, 2024)

Analyzing the effectiveness of quantum annealing with meta-learning. - Riccardo Pellini; Maurizio Ferrari Dacrema (Quantum Mach. Intell., 2024)

Overview of QuantumCLEF 2025: The Second Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. - Andrea Pasin; Maurizio Ferrari Dacrema; Washington Cunha; Marcos André Gonçalves; Paolo Cremonesi; Nicola Ferro 001 (CLEF, 2025)

QuantumCLEF 2025 - The Second Edition of the Quantum Computing Lab at CLEF. - Andrea Pasin; Maurizio Ferrari Dacrema; Paolo Cremonesi; Washington Cunha; Marcos André Gonçalves; Nicola Ferro 001 (ECIR (5), 2025)

A Worrying Reproducibility Study of Intent-Aware Recommendation Models. - Faisal Shehzad; Maurizio Ferrari Dacrema; Dietmar Jannach (SIGIR, 2025)

Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing. - Maurizio Ferrari Dacrema; Michael Benigni; Nicola Ferro 001 (CoRR, 2025)

Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch. - Michael Benigni; Maurizio Ferrari Dacrema; Dietmar Jannach (CoRR, 2025)

Automated Design of Structured Variational Quantum Circuits with Reinforcement Learning. - Gloria Turati; Simone Foderà; Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (CoRR, 2025)

Minor Embedding for Quantum Annealing with Reinforcement Learning. - Riccardo Nembrini; Maurizio Ferrari Dacrema; Paolo Cremonesi (CoRR, 2025)

Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms. - Filippo Brozzi; Gloria Turati; Maurizio Ferrari Dacrema; Filippo Caruso; Paolo Cremonesi (CoRR, 2025)

Impression-Aware Recommender Systems. - Fernando Benjamín Pérez Maurera; Maurizio Ferrari Dacrema; Pablo Castells; Paolo Cremonesi (Trans. Recomm. Syst., 2025)