Disentanglement and Interpretability in Recommender Systems
Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly corre...
10 Mars 30min
Collective Altruism in Recommender Systems
Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research r...
27 Feb 54min
Niche vs Mainstream
Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows spec...
18 Feb 34min
Healthy Friction in Job Recommender Systems
In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roa...
2 Feb 26min
Fairness in PCA-Based Recommenders
In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enter...
26 Jan 49min
Video Recommendations in Industry
In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...
26 Dec 202538min
Eye Tracking in Recommender Systems
In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...
18 Dec 202552min
Cracking the Cold Start Problem
In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...
8 Dec 202539min
