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 intersection of human curation and machine learning in content discovery, discussing the concept of "algatorial" curation—where algorithms and editorial expertise work together. Key topics include the cold start problem, why every metric is just a "proxy metric" for what users actually want, the challenge of filter bubbles, and the importance of balancing familiarity with discovery. Cory shares insights on why TikTok's algorithm works so well (clean data and massive interaction volume), the crucial role of homepage curation, and how human curators help by contextualizing content, cleaning data, and identifying positive feedback loops that algorithms might miss.

The conversation covers practical challenges like measuring "surprise and delight," the content deluge created by democratized creation tools, and why trust in tech companies is essential for better personalization. Cory emphasizes that discovery is "a good type of friction" and explains how the CODE framework (Capture, Organize, Distill, Express, plus Analysis) guides professional curation work. Looking to the future, they discuss the need for systems thinking that creates narrative connections between content, the potential for conversational AI to help users articulate preferences, and why diverse perspectives beyond engineering are crucial for building effective discovery systems. Resources mentioned include the newsletter "Top Information Retrieval Papers of the Week" and Notebook LM for synthesizing research.

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, Santiago explains the mechanics of eye tracking technology—how it captures gaze data and processes it into fixations and saccades to reveal user browsing patterns. He introduces the groundbreaking RecGaze dataset, the first eye tracking dataset specifically designed for recommender systems research, which opens new possibilities for understanding how users interact with carousel interfaces like Netflix. Through collaboration between psychologists and AI researchers, Santiago's work demonstrates how eye tracking can uncover insights about positional bias and user engagement that traditional click data misses.

Beyond the technical aspects, Santiago addresses the ethical considerations surrounding eye tracking data, particularly concerning pupil data and privacy. He emphasizes the importance of questioning assumptions in recommender systems and shares practical advice for improving recommendation algorithms by understanding actual user behavior rather than relying solely on click patterns. Looking forward, Santiago discusses exciting future directions including simulating user behavior using eye tracking data, addressing the cold start problem, and translating these findings to e-commerce applications. This conversation challenges researchers and practitioners to think more deeply about de-biasing clicks and leveraging eye tracking as a powerful tool to enhance user experience in recommendation systems.

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 apply XGBoost to tabular data, recommender systems require sophisticated hybrid approaches that combine multiple techniques. Our guest, Boya Xu, an assistant professor of marketing at Virginia Tech, walks us through a cutting-edge method that integrates three key components: collaborative filtering for dimensionality reduction, embeddings to represent users and items in latent space, and bandit learning to balance exploration and exploitation when deploying new recommendations.

Boya shares insights from her research on how recommender systems impact both consumers and content creators across e-commerce and social media platforms. We explore critical challenges like the cold start problem—how to make good recommendations for brand new users—and discuss how her approach uses demographic information to create informative priors that accelerate learning. The conversation also touches on algorithmic fairness, revealing how her method reduces bias between majority and minority (niche preference) users by incorporating active learning through bandit algorithms. Whether you're interested in the mathematics of recommendation engines or the broader implications for digital platforms, this episode offers a comprehensive look at the state-of-the-art in recommender system design.