Recommendation Systems
topic on 5 shows · 5 statements across 5 episodes
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5 statements about Recommendation Systems, every show
AppLovin Scrapped Its Core Ad Tech Stack in 2022 to Rebuild Around AI
“Well, in 22 at the very bottom, we said, we're on an older version of machine learning. We're going to completely throw out our technology, rebuild it, and go to what is really cutting edge and current and in the field of recommendation systems.”
Agrawal: Recommendation systems, search, and databases are converging into one
“My worldview is currently shaped, shaping into a convergence between recommendation systems and search systems and database systems. I think they're all going to start looking like the same thing.”
Liu: AI recommenders can sell $20 shirts but struggle with luxury narratives
“Narrative matters a lot to human beings. And I think the recommendation system, that's really hard to capture. Like, it's easy to sell, it's easy to use AI to sell, like, a 20 dollar shirt, but it's really hard for AI to sell, like, a 500 dollar shirt.”
Evans: Ungated, algorithm-free feeds inevitably collapse under content volume
“If you're building a recommendation system that says anybody can publish anything they like, and there's no gatekeeping, and there's no algorithms, guess what? You've got 10,000 things in your feed every day, and you can't find anything. It's like you're tryin…”