26 citations · 76 across the 11 of their papers we have counts for
11 papers
Distributionally-Informed Recommender System Evaluation
Michael D. Ekstrand, Ben Carterette, Fernando Diaz
Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additio…
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
Ali Vardasbi, Maarten de Rijke, Fernando Diaz +1
When learning to rank from user interactions, search and recommender systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recen…
Scaling Laws Do Not Scale
Fernando Diaz, Michael Madaio
Recent work has advocated for training AI models on ever-larger datasets, arguing that as the size of a dataset increases, the performance of a model trained on that dataset will c…
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro, Gustavo Ferreira, Fernando Diaz +1
Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimiz…
Striving for data-model efficiency: Identifying data externalities on group performance
Esther Rolf, Ben Packer, Alex Beutel +1
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predict…
Analyzing the Effect of Sampling in GNNs on Individual Fairness
Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi
Graph neural network (GNN) based methods have saturated the field of recommender systems. The gains of these systems have been significant, showing the advantages of interpreting d…
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro, Gustavo Ferreira, Fernando Diaz +1
Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of resea…
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Filip Radlinski, Krisztian Balog, Fernando Diaz +2
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understa…
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma +2
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of…
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Samira Shabanian, Fernando Diaz +2
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practice…
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
Haolun Wu, Chen Ma, Bhaskar Mitra +2
Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, howev…