Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. Nevertheless, only around half of the published studies are reproducible. When comparing the existing approaches under the same protocol, we get unexpected outcomes, such as the …
Category: Algorithmic fairness
Enabling cross-continent provider fairness in educational recommender systems
The courses of teachers are under-recommended by state-of-the-art models, unless they belong to the country that offers more courses and attracts more ratings. Regulating how recommendations are distributed with respect to the country of provenience of the teachers enables equitable and effective recommendations (cross-continent provider fairness). In a paper published in the Future Generation Computing …
Provider fairness across continents in collaborative recommender systems
In the presence of data imbalances, where some demographic groups of providers are represented more than others, the items of all the demographic groups that are not the majority group are under-recommended. A mitigation that accounts for the representation of each demographic group allows to introduce equity in the recommendation process, without having an impact …
The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems
The fact that most of the courses in MOOC platforms are offered by American teachers leads to the over-recommendation of these courses, at the expense of the courses produced in the other countries. A re-ranking that accounts for the country of production of a course, besides the relevance of the course for a user, is …
Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
In the presence of a minority group of item providers in the data (characterized by a sensitive attribute, such as gender or age), the items of these providers are considered as of lower relevance and are recommended to the users with a lower visibility (i.e., fewer times) and a lower exposure (i.e., in lower positions …
Disparate Impact in Item Recommendation: a Case of Geographic Imbalance
Data imbalances, related to the country of production of an item, lead to the under-recommendation of items produced in the smaller (less represented) countries. Re-ranking the recommendation lists, by balancing item relevance with the promotion of items produced in smaller countries can introduce equity in terms of visibility and exposure, without affecting recommendation effectiveness. In …
From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems
The size of a provider’s catalog in a platform affects the exposure that will be given to that provider by session-based recommender systems. Small providers, that are as popular as the big ones, are likely to get under-exposed in the recommendations. In an ECIR 2021 paper, with Alejandro Ariza, Francesco Fabbri, and Maria Salamó, we highlight side effects …
The effect of homophily on disparate visibility of minorities in people recommender systems
Demographics and homophily are the main drivers behind people recommendation in social networks and can affect the visibility that is given to users when they are recommended. These phenomena mainly impact users who belong to the minority groups, which have lower possibilities of being recommended, unless they are highly homophilic. In a recent ICWSM 2020 …