Data Skeptic

Anas Buhayh breaks down the S'mores framework—a radical approach to algorithmic pluralism where YOU choose which recommender serves your content.Horror fan? Soul-funk enthusiast? There's an algorithm for that.Listen now 🎧 open.spotify.com/episode/3BCenL…

Cory Zechmann, shares 16 years of wisdom on the art of "algatorial" curation—where human expertise meets machine learning.Why does TikTok work so well? What's the CODE framework? How do we balance discovery with familiarity? 🎧 open.spotify.com/episode/3pCBUK…

💼 AI-powered job matching sounds great... but can you trust the recommendations?Roan Schellingerhout discusses explainable recommender systems for recruitment—and why "healthy friction" might actually help users make better decisions.Listen 🎧 open.spotify.com/episode/5aXhhs…

Václav Blahut from seznam.cz explains "inverse recommendation"—finding the right users for niche content instead of the usual approach.A clever repurposing of two-tower models that gives long-tail content a fighting chance.Dive in 🎧 open.spotify.com/episode/6zB80r…

@ek8terina (@MIT) talks strategic learning in rec sys 🎯 The paradox: algorithmic "protest movements" can actually HELP platforms by providing clearer signals We explore game theory, coordinated user behavior, and the platform vs. user arms race open.spotify.com/episode/358hGx…

@ervindervishaj open.spotify.com/episode/2vu304…

Can recommender systems be both powerful AND interpretable? 🔍 @ervindervishaj (@UniCopenhagen) shares research on disentanglement in RecSys Key finding: strong correlation between disentanglement & interpretability, but not always with performance

🎵 How can music recommendations be fairer? @Rebeccasalganik, @UofR, presents LARP, a framework tackling popularity and multi-interest bias in playlist continuation. Her Music Semantics dataset captures how ppl describe music—atmosphere, context, vibes. 🎯open.spotify.com/episode/0eIvXG…

@ek8terina dataskeptic.com/blog/episodes/…

🧠 What happens when users coordinate to game recommendation algorithms? @ek8terina reveals her findings: algorithmic "protest movements" can paradoxically benefit platforms by providing clearer preference signals.

Can ML be unfair without bad intent? Yes. 🤔 @dayvidliu (@Cornell) shows how PCA over-specializes on popular content, neglecting niche users. But his item-weighted PCA improves BOTH fairness AND performance! ⚖️ #AlgorithmicFairness #ResponsibleAI open.spotify.com/episode/6IsPN6…

What if we tracked eyes, not just clicks? 👁️ Santiago reveals how eye tracking uncovers what users actually see in recommendations. Introducing RecGaze—the 1st eye tracking dataset for rec systems! Changes everything about positional bias. 🎬 #RecSys open.spotify.com/episode/15ZZsL…

Study recommendation algorithms without direct data access! Our guests present a "recommender neutral user model" to deduce algorithmic impact when exposure data is missing. This breakthrough aids in understanding complex social media systems. #RecSys 🎯 tinyurl.com/mw53hu53

📊 @alberto_mancino of @sisinflab talks DataRec—a Python library solving dataset management headaches in recommender systems research. Automated downloads, checksum verification, standardized filtering. Game-changer for reproducibility! 🚀 #RecSys #MLOps open.spotify.com/episode/1r4nFw…

Can we build greener AI without losing performance? 🌱 @AntonioPuri00 of @SapienzaRoma talks EcoAware Graph Neural Networks—measuring and reducing the enviro impact of recommender systems. Learn to make your ML models more sustainable! 🌍 #GreenAI #RecSys open.spotify.com/episode/5ew0Nj…

How do fake profiles game recommendation algorithms? 🎯 @aditya_chichani from Walmart breaks down shilling attacks—from Spotify playlist manipulation to fake product reviews. Essential listening for anyone building rec systems! #MachineLearning #RecSys open.spotify.com/episode/5ecvTT…

🌍 Can AI make tourism more sustainable? @ashmi_banerjee from @TU_Muenchen talks using recommender systems to promote responsible travel. Learn how LLMs make synthetic tourism data & how algorithms can balance traveler satisfaction with enviro impact. 🎯 open.spotify.com/episode/0jcswV…

@Vida19231939 open.spotify.com/episode/0OIYxU…

🚀 The cold start problem is REAL! @Vida19231939 breaks down how hybrid recommender systems combine collaborative filtering + embeddings + bandit learning to make great recommendations for brand new users 🎧️ Listen👇 #MachineLearning #RecommenderSystems #DataScience #AI

🎙️ Check out Kyle's recent interview with @center4inquiry! He dives into AI, skepticism, and how technology intersects with critical thinking—covering everything from recommendation systems to the future of AI development. 🤖🔍