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)

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)

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)

Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems. - Vincenzo Paparella (RecSys, 2022)

Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation. - Vincenzo Paparella; Vito Walter Anelli; Franco Maria Nardini; Raffaele Perego 001; Tommaso Di Noia (CIKM, 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)

Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives. - Vincenzo Paparella; Vito Walter Anelli; Ludovico Boratto; Tommaso Di Noia (RecSys, 2023)

Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy. - Vincenzo Paparella; Dario Di Palma; Vito Walter Anelli; Tommaso Di Noia (RecSys, 2023)

A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction. - Paolo Sorino; Vincenzo Paparella; Domenico Lofù; Tommaso Colafiglio; Eugenio Di Sciascio; Fedelucio Narducci; Rodolfo Sardone; Tommaso Di Noia (SMC, 2023)

Unveiling the Potential of Recommender Systems through Multi-Objective Metrics. - Vincenzo Paparella; Dario Di Palma; Vito Walter Anelli; Alessandro De Bellis; Tommaso Di Noia (IIR, 2024)

Legal but Unfair: Auditing the Impact of Data Minimization on Fairness and Accuracy Trade-off in Recommender Systems. - Salvatore Bufi; Vincenzo Paparella; Vito Walter Anelli; Tommaso Di Noia (UMAP, 2025)