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FIDELITY

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Fairness and Explanations in Group Recommender Systems. Towards Trustworthiness...

Fairness and Explanations in Group Recommender Systems. Towards Trustworthiness and transparency Today, most social media networks use automated tools to recommend content or products and to rank, curate and moderate posts. Recommender systems (RSs), and in particular Group recommender systems (GRSs), -a specific kind of RSs... ver más

30/04/2026

UJA

181K€

Presupuesto del proyecto: 181K€

Líder del proyecto

UNIVERSIDAD DE JAÉN No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 981

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-07-17

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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4 Participantes

UJA

181.15K€ | Lider

BIOLIZA

N.D. | Participante

UNIVERSITEIT MAASTRICHT

N.D. | Participante

EMERGYA INGENIERÍA

N.D. | Participante

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Duración del proyecto: 33 meses Fecha Inicio: 2023-07-17
Fecha Fin: 2026-04-30

Líder del proyecto

UNIVERSIDAD DE JAÉN No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 981

Presupuesto del proyecto 181K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Today, most social media networks use automated tools to recommend content or products and to rank, curate and moderate posts. Recommender systems (RSs), and in particular Group recommender systems (GRSs), -a specific kind of RSs used to recommend items to a group of users-, are likely to become more ubiquitous, with expected market forecast to reach USD 16.13 billion by 2026. These automated content governance tools are receiving emerging interest as both algorithms and decision-making processes behind the platforms are not sufficiently transparent, with a negative impact on domains such as fair job opportunities, fair e-commerce or news exposure. Two of the key requirements that have to be fulfilled to build and keep users’ trust in AI systems while guaranteeing transparency are Fairness and Explainability. But, aside from some previous attempts to enhance both aspects in traditional-individual RS, they have hardly been explored in GRSs. FIDELITY addresses this challenge by developing novel algorithms and computational tools in GRS to boost explanation, fairness, and synergy between them through a disruptive multidisciplinary research approach that: 1) extensively brings SHAP and LIME, as state-of-the-art post-hoc explanation approaches in AI, into RS and GRS contexts, 2) bridges explanation and fairness in RS and GRS, introducing an explanation paradigm shift moving from why are the recommendations generated? to how fair are the generated recommendations? and, 3) transversally evaluates the new methods through real-world GRSs and user studies. The ultimate goal is to guarantee greater user trust, and independence of RS output from any of the sociodemographic characteristics of users. The training programme, designed with the aim to fill the existing gaps between computing science, social research and business development reality, will provide the candidate with a multidisciplinary background that will boost his innovation potential and career prospects.

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