Happy to announce that, a couple weeks ago, I started a new job as a Machine Learning Engineer at Instagram, specifically working on Threads! ๐
ใใใฆๆฅๆฌ่ชใงๅใ็จใฎใขใซใฆใณใใในใฌใใบใงๅใใพใใใใใใใฐใใฃใกใง็นใใฃใฆใใ ใใ!ใhttps://t.co/EecV0Jbpf8
I had the pleasure of giving a lecture about recommender systems at Columbia's Applied ML class! Hopefully students got some clarity on what I think is a criminally under-discussed topic in college education despite its prevalence in the industry.
Talk title: "FitNExT: Leveraging Transformers with Context-Augmented Start Tokens to Generate Recommendations for New Users in Connected Fitness at Peloton"
Very excited to be in Singapore for #Recsys2023! Come check out my presentation on how we tackled the user cold-start problem at Peloton at the BehavRec workshop, 9/19 9:10 - 9:30 SGT!
#RecSys2022 Highlight #3 - Industry Keynote of Context-Aware Recommender system (CARS) Workshop - "Contextual Product Recommendation at Wayfair" by Jeffrey Mei. Presentation Slides: https://t.co/0oj1ITTQya
This also begs the question - why not just always have the item that the user ends up buying as the positive training data? Well, they did try that, and it so turned out that the users didn't like that because users want to explore a little first (which makes sense)!