Four Key Factors to Design a Web of Things Architecture. - Francesco Bruni; Claudio Pomo; Gaetano Murgolo (ICWE Workshops, 2017)

On fast prototyping LoRaWAN: a cheap and open platform for daily experiments. - Mariano Pulpito; Paolo Fornarelli; Claudio Pomo; Pietro Boccadoro; Luigi Alfredo Grieco (IET Wirel. Sens. Syst., 2018)

Semantic interpretability of latent factors for recommendation. - Vito Walter Anelli; Tommaso Di Noia; Eugenio Di Sciascio; Azzurra Ragone; Claudio Pomo (IIR, 2019)

On the discriminative power of hyper-parameters in cross-validation and how to choose them. - Vito Walter Anelli; Tommaso Di Noia; Eugenio Di Sciascio; Claudio Pomo; Azzurra Ragone (RecSys, 2019)

GUapp: A Conversational Agent for Job Recommendation for the Italian Public Administration. - Vito Bellini; Giovanni Maria Biancofiore; Tommaso Di Noia; Eugenio Di Sciascio; Fedelucio Narducci; Claudio Pomo (EAIS, 2020)

Towards a Trustworthy Patient Home-Care Thanks to an Edge-Node Infrastructure. - Carmelo Ardito; Tommaso Di Noia; Eugenio Di Sciascio; Domenico Lofù; Giulio Mallardi; Claudio Pomo; Felice Vitulano (HCSE, 2020)

Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems. - Giandomenico Cornacchia; Francesco M. Donini; Fedelucio Narducci; Claudio Pomo; Azzurra Ragone (CAiSE Workshops, 2021)

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation. - Vito Walter Anelli; Alejandro Bellogín; Antonio Ferrara 001; Daniele Malitesta; Felice Antonio Merra; Claudio Pomo; Francesco Maria Donini; Eugenio Di Sciascio; Tommaso Di Noia (IIR, 2021)

Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications. - Vito Walter Anelli; Luca Belli; Yashar Deldjoo; Tommaso Di Noia; Antonio Ferrara 001; Fedelucio Narducci; Claudio Pomo (RecSys, 2021)

V-Elliot: Design, Evaluate and Tune Visual Recommender Systems. - Vito Walter Anelli; Alejandro Bellogín; Antonio Ferrara 001; Daniele Malitesta; Felice Antonio Merra; Claudio Pomo; Francesco Maria Donini; Tommaso Di Noia (RecSys, 2021)

Adherence and Constancy in LIME-RS Explanations for Recommendation (Long paper). - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Francesco Maria Donini; Vincenzo Paparella; Claudio Pomo (KaRS/ComplexRec@RecSys, 2021)

Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization. - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Claudio Pomo (RecSys, 2021)

On the Need for a Body of Knowledge on Recommender Systems (Short paper). - Juri Di Rocco; Davide Di Ruscio; Claudio Di Sipio; Phuong Thanh Nguyen 001; Claudio Pomo (KaRS/ComplexRec@RecSys, 2021)

Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. - Vito Walter Anelli; Alejandro Bellogín; Antonio Ferrara 001; Daniele Malitesta; Felice Antonio Merra; Claudio Pomo; Francesco Maria Donini; Tommaso Di Noia (SIGIR, 2021)

Reenvisioning Collaborative Filtering vs Matrix Factorization. - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Claudio Pomo (CoRR, 2021)

Adherence and Constancy in LIME-RS Explanations for Recommendation. - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Francesco Maria Donini; Vincenzo Paparella; Claudio Pomo (CoRR, 2021)

Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews. - Vito Walter Anelli; Yashar Deldjoo; Tommaso Di Noia; Eugenio Di Sciascio; Antonio Ferrara 001; Daniele Malitesta; Claudio Pomo (DL4SR@CIKM, 2022)

Towards a theoretical formalization of conversational recommendation. - Tommaso Di Noia; Francesco Maria Donini; Dietmar Jannach; Fedelucio Narducci; Claudio Pomo (CIKM Workshops, 2022)

An Analysis of Local Explanation with LIME-RS. - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Francesco Maria Donini; Vincenzo Paparella; Claudio Pomo (IIR, 2022)

How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. - Vito Walter Anelli; Yashar Deldjoo; Tommaso Di Noia; Eugenio Di Sciascio; Antonio Ferrara 001; Daniele Malitesta; Claudio Pomo (MORS@RecSys, 2022)

The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models. - Vito Walter Anelli; Alejandro Bellogín; Antonio Ferrara 001; Daniele Malitesta; Felice Antonio Merra; Claudio Pomo; Francesco M. Donini; Eugenio Di Sciascio; Tommaso Di Noia (SEBD, 2022)

Top-N Recommendation Algorithms: A Quest for the State-of-the-Art. - Vito Walter Anelli; Alejandro Bellogín; Tommaso Di Noia; Dietmar Jannach; Claudio Pomo (UMAP, 2022)

Conversational recommendation: Theoretical model and complexity analysis. - Tommaso Di Noia; Francesco Maria Donini; Dietmar Jannach; Fedelucio Narducci; Claudio Pomo (Inf. Sci., 2022)

Irrelevant Explanations: a Logical Formalization and a Case Study. - Simona Colucci; Tommaso Di Noia; Francesco M. Donini; Claudio Pomo; Eugenio Di Sciascio (XAI.it@AI*IA, 2023)

Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. - Vito Walter Anelli; Yashar Deldjoo; Tommaso Di Noia; Daniele Malitesta; Vincenzo Paparella; Claudio Pomo (ECIR (1), 2023)

Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. - Dario Di Palma; Vito Walter Anelli; Daniele Malitesta; Vincenzo Paparella; Claudio Pomo; Yashar Deldjoo; Tommaso Di Noia (IIR, 2023)

Knowledge Graph Datasets for Recommendation. - Vincenzo Paparella; Alberto Carlo Maria Mancino; Antonio Ferrara 001; Claudio Pomo; Vito Walter Anelli; Tommaso Di Noia (KaRS@RecSys, 2023)

EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments. - Federico Bianchi 001; Patrick John Chia; Jacopo Tagliabue; Ciro Greco; Gabriel de Souza P. Moreira; Davide Eynard; Fahd Husain; Claudio Pomo (KDD, 2023)

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Tommaso Di Noia (EvalRS@KDD, 2023)

Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. - Daniele Malitesta; Giuseppe Gassi; Claudio Pomo; Tommaso Di Noia (ACM Multimedia, 2023)

On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Tommaso Di Noia (MMIR@MM, 2023)

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. - Vito Walter Anelli; Daniele Malitesta; Claudio Pomo; Alejandro Bellogín; Eugenio Di Sciascio; Tommaso Di Noia (RecSys, 2023)

An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework. - Daniele Malitesta; Claudio Pomo; Vito Walter Anelli; Tommaso Di Noia; Antonio Ferrara 001 (UMAP (Adjunct Publication), 2023)

A Topology-aware Analysis of Graph Collaborative Filtering. - Daniele Malitesta; Claudio Pomo; Vito Walter Anelli; Alberto Carlo Maria Mancino; Eugenio Di Sciascio; Tommaso Di Noia (CoRR, 2023)

Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation. - Daniele Malitesta; Claudio Pomo; Tommaso Di Noia (CoRR, 2023)

Auditing fairness under unawareness through counterfactual reasoning. - Giandomenico Cornacchia; Vito Walter Anelli; Giovanni Maria Biancofiore; Fedelucio Narducci; Claudio Pomo; Azzurra Ragone; Eugenio Di Sciascio (Inf. Process. Manag., 2023)

Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? - Daniele Malitesta; Emanuele Rossi; Claudio Pomo; Tommaso Di Noia; Fragkiskos D. Malliaros (CIKM, 2024)

Toward Effective Fashion Item Compatibility Modelling. - Matteo Attimonelli; Claudio Pomo; Dietmar Jannach; Tommaso Di Noia (IIR, 2024)

DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation. - Antonio Ferrara 001; Marco Valentini; Paolo Masciullo; Antonio De Candia; Davide Abbattista; Riccardo Fusco; Claudio Pomo; Vito Walter Anelli; Giovanni Maria Biancofiore; Ludovico Boratto; Fedelucio Narducci (RecSys Challenge, 2024)

RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys, 2024)

EB-NeRD a large-scale dataset for news recommendation. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys Challenge, 2024)

A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. - Daniele Malitesta; Claudio Pomo; Vito Walter Anelli; Alberto Carlo Maria Mancino; Tommaso Di Noia; Eugenio Di Sciascio (RecSys, 2024)

Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. - Matteo Attimonelli; Danilo Danese; Daniele Malitesta; Claudio Pomo; Giuseppe Gassi; Tommaso Di Noia (WWW (Companion Volume), 2024)

Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach. - Daniele Malitesta; Emanuele Rossi; Claudio Pomo; Fragkiskos D. Malliaros; Tommaso Di Noia (CoRR, 2024)

Fashion Image-to-Image Translation for Complementary Item Retrieval. - Matteo Attimonelli; Claudio Pomo; Dietmar Jannach; Tommaso Di Noia (CoRR, 2024)

Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation. - Matteo Attimonelli; Danilo Danese; Angela Di Fazio; Daniele Malitesta; Claudio Pomo; Tommaso Di Noia (CoRR, 2024)

DataRec: A Framework for Standardizing Recommendation Data Processing and Analysis. - Alberto Carlo Maria Mancino; Salvatore Bufi; Angela Di Fazio; Daniele Malitesta; Claudio Pomo; Antonio Ferrara 001; Tommaso Di Noia (CoRR, 2024)

A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders. - Vito Bellini; Eugenio Di Sciascio; Francesco Maria Donini; Claudio Pomo; Azzurra Ragone; Angelo Schiavone (J. Intell. Inf. Syst., 2024)

Enhancing Reproducibility and Replicability in Information Retrieval: A Path Towards Scientific Integrity and Effective Research. - Antonio Ferrara 001; Claudio Pomo; Nicola Tonellotto (ECIR (5), 2025)

DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems. - Alberto Carlo Maria Mancino; Salvatore Bufi; Angela Di Fazio; Antonio Ferrara 001; Daniele Malitesta; Claudio Pomo; Tommaso Di Noia (SIGIR, 2025)

Enhancing News Recommendation Systems Using Multitask Learning. - Paolo Masciullo; Andrea Pazienza; Claudio Pomo; Fedelucio Narducci; Tommaso Di Noia (UMAP (Adjunct Publication), 2025)

Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search. - Matteo Attimonelli; Alessandro De Bellis; Claudio Pomo; Dietmar Jannach; Eugenio Di Sciascio; Tommaso Di Noia (CoRR, 2025)

Formalizing Multimedia Recommendation through Multimodal Deep Learning. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Felice Antonio Merra; Tommaso Di Noia; Eugenio Di Sciascio (Trans. Recomm. Syst., 2025)