Recommender Systems

The're not just for eCommerce. Recommender systems have infiltrated our video watching, travel planning, social media feeds, and other daily activities. This season's interviews explore the methods, implementations, and consequences of these ubiquitous systems.

Episodes

• Social Choice for Fair Recommendations — Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems,
• News Recommendations — News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI,
• Give Users the Wheel — What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines
• AutoLike — How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explor
• Student Spotlight: Aaron Payne, Data Analyst — Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They
• The Future is Agentic in Recommender Systems — Kyle Polich sits down with Yashar Deldjoo, Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key d
• Book Ratings and Recommendations — Goodreads star ratings can be misleading as measures of “book quality,” and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often mat
• 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
• 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
• 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
• Healthy Friction in Job Recommender Systems — Host Kyle Polich interviews Roan Schellingerhout, a PhD student researching explainable AI-powered job matching systems that balance the needs of job seekers, recruiters, and companies, with findings
• 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
• 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 "A Silence No Good." They explore the in
• 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,
• 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
• Designing Recommender Systems for Digital Humanities — In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te
• DataRec Library for Reproducible in Recommend Systems — In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea
• Shilling Attacks on Recommender Systems — In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm
• Music Playlist Recommendations — In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste
• Bypassing the Popularity Bias — In this episode, we speak with Václav Blahut, a machine learning researcher at Seznam.cz, about tackling popularity bias in recommender systems. Václav explains inverse recommendation—finding the righ
• Sustainable Recommender Systems for Tourism — In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates
• Interpretable Real Estate Recommendations — In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpr
• Why am I seeing This? — In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri
• Eco-aware GNN Recommenders — In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks
• Networks and Recommender Systems — Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field