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 …
Category: Recommender systems
Bias characterization, assessment, and mitigation in location-based recommender systems
Location-based recommender systems (LBRSs) provide suggestion for Points of Interest (POIs) in Location-based social networks. However, we can characterize different forms of bias, associated with polarized interactions of the users with the PoIs. Post-processing and hybrid mitigation approaches can help alleviate the impact of those biases. In a study, published in the Data Mining and …
Reinforcement recommendation reasoning through knowledge graphs for explanation path quality
Knowledge Graph-based recommender systems naturally produce explainable recommendations, by showing the reasoning paths in the knowledge graph (KG) that were followed to select the recommended items. One can define metrics that assess the quality of the explanation paths in terms of recency, popularity, and diversity. Combining in- and post-processing approaches to optimize for both recommendation …
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 …
Hands on Explainable Recommender Systems with Knowledge Graphs
This tutorial was presented at the RecSys ’22 conference, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras. On the tutorial’s website, you can find the slides, the video recording of our talk, and the notebooks of the hands-on parts. The goal of this tutorial was to present the RecSys community with recent advances on explainable …
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 …
Regulating Group Exposure for Item Providers in Recommendation
Platform owners can seek to guarantee certain levels of exposure to providers (e.g., to bring equity or to push the sales of new providers). Rendering certain groups of providers with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility. In a SIGIR 2022 paper, with Mirko Marras, Guilherme Ramos, and …
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 …