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Transformer-based Realtime User Action Model for Recommendation at Pinterest
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Tech Talks 17
by Pong Eksombatchai
One of the primary engineering challenges at Pinterest is how to help people discover ideas they want to try, which means serving the right idea to the right person at the right time. While most other recommender systems have a small pool of possible candidates (like 100,000 film titles on a movie review site), Pinterest has to recommend from a catalog of more than 4+ billion ideas. To make it happen, we built Pixie, a flexible, graph-based system for making personalized recommendations in real-time.
http://about.pinterest.com/
iTunes App Store: http://pin.it/VQ-xmlR
Google Play: http://pin.it/bEYNSEA
by Ekrem Kocaguneli
Welcome to Pinterest’s home-sweet-Home Feed. Ekrem starts by giving you the ins and outs of how Pinterest’s highly personalized Home Feed works, then explains how we use machine learning techniques to rank the Pins you find there and fully personalize the experience.
http://about.pinterest.com/
iTunes App Store: http://pin.it/VQ-xmlR
Google Play: http://pin.it/bEYNSEA
by Justin Mejorada-Pier & Charlie Gu
In this talk, Justin and Charlie run through the challenges they faced while building in-house tools like DataHub (the primary way people run queries and mine data here at Pinterest). Tune in as they share the hard-won learnings they picked up along the way.
http://about.pinterest.com/
iTunes App Store: http://pin.it/VQ-xmlR
Google Play: http://pin.it/bEYNSEA
by Jenny Liu
Learn how we built the web-scale recommender system that powers over 40% of user engagement on Pinterest. Jenny will discuss how the small but mighty team prioritized the simplest and highest-leverage solutions. She’ll also give a rundown of the many challenges and learnings that came up in the evolution of candidate generation, Memboost and ranking in our system.
http://about.pinterest.com/
iTunes App Store: http://pin.it/VQ-xmlR
Google Play: http://pin.it/bEYNSEA
