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 …
Category: Algorithmic fairness
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 …