Search Results for author: Lukasz Heldt

Found 2 papers, 0 papers with code

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

no code implementations14 Oct 2022 Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel

We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.

Fairness Recommendation Systems

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

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