Recommender Systems Today and Tomorrow
In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control.
Podcast profile
Data Skeptic
By Kyle Polich
A complete show description is available below.
Latest episode
Recommender Systems Today and Tomorrow
23 min
- Cadence
- Weekly
- Typical length
- 35 min
- Latest release
- Sep 9
- Language
- EN
- Activity
- Current — released within 30 days
Podcast Classification
Topics
Product Reviews
Evaluation, comparison, recommendation, and practical assessment of technology products or media.
Data Engineering
Building and operating data pipelines, transformations, orchestration, and data platforms.
Machine Learning
Model development, learning methods, prediction systems, and applied machine learning.
Formats
Interview
Host-led conversation where a guest supplies most of the subject matter.
Technical Deep Dive
Detailed technical, architectural, or research-oriented examination.
Audience level
Advanced
Regularly assumes specialist knowledge and discusses implementation, architecture, protocols, code, or research in depth.
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Episodes shown — 10 of 25
Recommender Systems Optimization Goals
In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for?
Recommender Systems Origin Story
Where did recommender systems come from, and how do we know when they're actually working?
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?
News Recommendations
News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world.
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?
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 explores how platforms like TikTok personalize content feeds.
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.
The Future is Agentic in Recommender Systems
Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters.
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 matter more than differences between books.