It is possible to effectively address consumer unfairness in recommender systems by using counterfactual explanations to augment the user-item interaction graph. This approach not only leads to fairer outcomes across different demographic groups but also maintains or improves the overall utility of the recommendations. In a study with Francesco Fabbri, Gianni Fenu, Mirko Marras, and …
Category: Algorithmic fairness
Practical perspectives of consumer fairness in recommendation
The mitigation of consumer fairness assumes that recommendations bring equitable effectiveness for the different demographic groups of users. Mitigation approaches can be analyzed from, multiple, technical perspectives. Different mitigation strategies at the state of the art offer different properties. In a study, published by the Information Processing and Management journal (Elsevier) and conducted with Gianni …
Fair performance-based user recommendation in eCoaching systems
When ranking sportspeople so that a coach can assist those who are in need, users of different genders might be affected by disparate exposure, meaning that the users in the minority group are systematically ranked in lower positions. A re-ranking can help mitigate disparities, without affecting recommendation quality. In a study, published by the User …
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling
User profiling approaches that model the interaction between users and items (behavioral user profiling) via Graph Neural Networks (GNNs) are unfair toward certain demographic groups. In a CIKM 2022 study, conducted with Erasmo Purificato and Ernesto William De Luca, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact …
Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations
The formalization of the learning opportunities that should be offered by the recommendation of online courses can lead to defining what fairness means for a platform. A post-processing approach that balances personalization and equality of recommended opportunities can lead to effective and fair recommendations. In a study published by the International Journal of Artificial Intelligence …
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Being able to assess explanation quality in recommender systems and by shaping recommendation lists that account for explanation quality allows us to produce more effective recommendations. These recommendations can also increase explanation quality according to the proposed properties, fairly across demographic groups. In a SIGIR 2022 paper, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras, …
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
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 …
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 …