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 …
Author: borattoludovico
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 …
Enabling Reproducibility in Group Recommender Systems
Group recommender systems produce suggestions in contexts in which more than one person is involved in the recommendation process. They present additional tasks w.r.t. those for single users, such as the identification of the groups, or their modeling. While this clearly amplifies the possible reproducibility issues, to date, no framework to benchmark group recommender systems …
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 …
Robust reputation independence in ranking systems for multiple sensitive attributes
Ranking systems that account for the reputation of the users can be biased towards different demographic groups, especially when considering multiple sensitive attributes (e.g., gender and age). Providing guarantees of reputation independence can lead to unbiased and effective rankings. Moreover, these rankings are also robust to attacks. In a study, published by the Machine Learning …
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 …