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2020 – today
- 2024
[c11]Vincenzo Paparella, Dario Di Palma, Vito Walter Anelli, Alessandro De Bellis, Tommaso Di Noia:
Unveiling the Potential of Recommender Systems through Multi-Objective Metrics. IIR 2024: 119-122- 2023
- [c10]Vincenzo Paparella
, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia:
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation. CIKM 2023: 2013-2023 - [c9]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo:
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. ECIR (1) 2023: 33-48 - [c8]Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia:
Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. IIR 2023: 79-84 - [c7]Vincenzo Paparella, Alberto Carlo Maria Mancino, Antonio Ferrara, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia:
Knowledge Graph Datasets for Recommendation. KaRS@RecSys 2023: 109-117 - [c6]Vincenzo Paparella, Vito Walter Anelli, Ludovico Boratto, Tommaso Di Noia:
Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives. RecSys 2023: 467-478 - [c5]Vincenzo Paparella, Dario Di Palma, Vito Walter Anelli, Tommaso Di Noia:
Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy. RecSys 2023: 1139-1145 - [c4]Paolo Sorino, Vincenzo Paparella, Domenico Lofù, Tommaso Colafiglio, Eugenio Di Sciascio, Fedelucio Narducci, Rodolfo Sardone, Tommaso Di Noia:
A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction. SMC 2023: 3822-3827 - [i2]Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia:
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation. CoRR abs/2306.12165 (2023) - 2022
- [c3]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
An Analysis of Local Explanation with LIME-RS. IIR 2022 - [c2]Vincenzo Paparella:
Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems. RecSys 2022: 727-729 - 2021
- [c1]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
Adherence and Constancy in LIME-RS Explanations for Recommendation (Long paper). KaRS/ComplexRec@RecSys 2021 - [i1]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
Adherence and Constancy in LIME-RS Explanations for Recommendation. CoRR abs/2109.00818 (2021)
Coauthor Index
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