Super excited this work is getting out! a summary of the work:
- Recommender engines are serving as a window into the world by regulating what content we see on a daily basis.
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I compiled my notes from #recsys2021 into a blogpost: impressions, current trends in recsys research, my favorite papers and highlights from other interesting works. https://t.co/nCnesi5gFD @gravityrd@domonkostikk
An early draft of the machine learning interviews book is out 🥳
The book is open-sourced and free. Job search is a stressful process, and I hope that this effort can help in some way.
Contributions and feedback are appreciated!
https://t.co/N1m3kNvZfo
Cool to see our ML and ML Platform reps talk about how we build ML @ Spotify! Cool insights on how we leverage an engagement team, layer our ML for product and build for the user needs!
Way to go Lex, @Jiminy_Kirket, Maisha, @mayahhf and @unclesam
https://t.co/1jMzy7r8q0
RecSys2020 (22-26 Sep) gave a peek into recent ideas on recommenders from academia & industry.
Some takeaways:
• Emphasis on ethics & bias
• Offline evaluation is tricky
• Dot product > learned similarities
• Many examples of real-world recsys
More👇 https://t.co/XAOl8WsU3o
When talking to people who haven’t deployed ML models, I keep hearing a lot of misperceptions about ML models in production. Here are a few of them.
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Sharing widely the slides of our #KDD2020 "Tutorial on Online User Engagement: Metrics and Optimization", which @hongliangjie and I gave on Sunday. Thank you for attending, your questions and feedbacks 🙏
https://t.co/g5lPtFsT1X
The Bias-Variance Trade-Off & "DOUBLE DESCENT" 🧵
Remember the bias-variance trade-off? It says that models perform well for an "intermediate level of flexibility". You've seen the picture of the U-shape test error curve.
We try to hit the "sweet spot" of flexibility.
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Only 1 more week to submit to #PodRecs: The Workshop on Podcast Recommendations @ACMRecSys! This is a relatively new field covering broad topics of #RecSys, NLP, KDD, speech/audio, fairness, evaluation and more. Don't be shy, apply! @PodRecSys#RecSys2020 https://t.co/Rxk0ckttUC
Interested in Causal Inference & Reinforcement Learning? Consider attending my @icmlconf tutorial on the basic principles & tools of Causal Reinforcement Learning (CRL). I’ll discuss many new & pervasive learning challenges/opportunities within CRL. Link: https://t.co/jXWmCYXvEA
This is such an important tweet for new researchers: A Turing award winner’s public admission that failure is ok. It’s through trying and failing (ie falsifying some hypotheses) that we make scientific progress. Thanks Geoff for setting a brilliant example.