Stereotype-Aaware Collaborative Filtering

• Autorzy: Frisch Gabriel , Leger Jean-Benoist , Grandvalet Yves

Identyfikatory

DOI

10.15439/2021F117

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

In collaborative filtering, recommendations are made using user feedback on a few products. In this paper, we show that even if sensitive attributes are not used to fit the models, a disparate impact may nevertheless affect recommendations. We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of sensitive attribute. We design a co-clustering of users and items that processes exogenous sensitive attributes to remove their influence to return fair recommendations. We prove that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.

Słowa kluczowe

EN

collaborative filtering; recommender systems; feedback

PL

filtrowanie zespołowe; systemy rekomendujące; odpowiedź zwrotna

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 69--79
• Opis fizyczny: Bibliogr. 35 poz., wz., rys., wykr.

Twórcy

autor

Frisch Gabriel

• Université de Technologie de Compiègne Heudiasyc Laboratory, UMR UTC/CNRS 7253

autor

Leger Jean-Benoist

• Université de Technologie de Compiègne Heudiasyc Laboratory, UMR UTC/CNRS 7253

autor

Grandvalet Yves

• Université de Technologie de Compiègne Heudiasyc Laboratory, UMR UTC/CNRS 7253

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Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications