Official twitter account for the Data Skeptic podcast, hosted by @kpolich. Find us on iTunes, Google Music, Stitcher, Pandora, Youtube...
Joined June 2014
- Tweets 2,256
- Following 703
- Followers 7,490
- Likes 1,670
356 Photos and videos
Mar 24
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/3BC…
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Mar 23
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/3pC…
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Mar 20
💼 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/5aX…
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Mar 19
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/6zB…
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Mar 17
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
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Mar 11
🎵 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/0eI…
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Mar 10
🧠 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.
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Mar 10
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Mar 9
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/6Is…
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Mar 6
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/15Z…
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Mar 5
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
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Mar 4
📊 @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/1r4…
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Mar 3
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/5ew…
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Mar 2
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/5ec…
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Feb 27
🌍 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/0jc…
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Feb 27
🚀 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
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Feb 25
🎙️ 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. 🤖🔍
“The Terminator 2 solution”: fight AI with AI.
@DataSkeptic's @kpolich on why the panic cycle misses the point and what to worry about instead: youtu.be/ePHS083n67c
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Feb 25
🏡 How do you find the next hot neighborhood? Graph neural networks may be the answer. @kunmukh from @Virginia_Tech talks about Z-REx, a GNN approach that recommends real estate regions and explains why—crucial for transparency in property search. 🎯 open.spotify.com/episode/6hj…
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