Building Production Recommender Systems - Maciej Kula - WEB2DAY 2017

MACIEJ KULA - MACHINE LEARNER ENGINEER @RAVELIN Building production recommender systems is a challenging task. In this talk, I’ll talk about the challenges I have encountered in doing so — from modelling choices (do published approaches fit your domain?), through system architecture (is your system fast enough to support online prediction?), to choosing the right evaluation metric (is clickthrough what you care about?) ————————— The Web2day festival is 3 intensive and festive days dedicated to digital trends and innovations. It’s a unique opportunity to meet with European startups, investors, media, influencers, big corporates in an exceptional setting and a relaxed atmosphere.   En savoir plus sur le Web2day et réservez vos places pour la prochaine édition : http://www.web2day.co Le Web2day est un événement organisé par La Cantine : http://www.atlantic2.org Crédit vidéo : Réalisation, Motion design & Scénographie : Mstream http://www.mstream.fr Musique Originale by Jasper Louise http://www.jasperlouise.com Enregistré à Stereolux http://www.stereolux.org

Maciej Kula - Hybrid Recommender Systems in Python
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Maciej Kula - Hybrid Recommender Systems in Python

Maciej Kula | Neural Networks for Recommender Systems
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Maciej Kula | Neural Networks for Recommender Systems

Recommendation Systems / Engines with TensorFlow - Google Cloud Platform User Group Singapore
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Recommendation Systems / Engines with TensorFlow - Google Cloud Platform User Group Singapore

Building a Real-time Recommendation Engine With Neo4j - Part 1/4 - William Lyon - OSCON 2017
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Building a Real-time Recommendation Engine With Neo4j - Part 1/4 - William Lyon - OSCON 2017

RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations
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RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations

Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026
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Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026

47th #ebaytechtalk: Deep Learning for Recommender Systems
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47th #ebaytechtalk: Deep Learning for Recommender Systems

RecSys 2020 Tutorial: Introduction to Bandits in Recommender Systems
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RecSys 2020 Tutorial: Introduction to Bandits in Recommender Systems

How to Design and Build a Recommendation System Pipeline in Python (Jill Cates)
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How to Design and Build a Recommendation System Pipeline in Python (Jill Cates)

Casey Muratori – The Big OOPs: Anatomy of a Thirty-five-year Mistake – BSC 2025
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Casey Muratori – The Big OOPs: Anatomy of a Thirty-five-year Mistake – BSC 2025

Deep Learning for Personalized Search and Recommender Systems part 1
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Deep Learning for Personalized Search and Recommender Systems part 1

RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems
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RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems

Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker
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Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

Recommender Systems
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Recommender Systems

Trends in Recommendation & Personalization at Netflix
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Trends in Recommendation & Personalization at Netflix

PyParis 2017 - Collaborative filtering for recommendation systems in Python, by N. Hug
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PyParis 2017 - Collaborative filtering for recommendation systems in Python, by N. Hug

Maciej Arciuch, Karol Grzegorczyk: Embeddings! Embeddings everywhere! | PyData London 2019
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Maciej Arciuch, Karol Grzegorczyk: Embeddings! Embeddings everywhere! | PyData London 2019

Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!
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Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!

"Reinforcement Learning for Recommender Systems: A Case Study on Youtube," by Minmin Chen
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"Reinforcement Learning for Recommender Systems: A Case Study on Youtube," by Minmin Chen

Deep Learning for Recommender Systems | Alexandros Karatzoglou
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Deep Learning for Recommender Systems | Alexandros Karatzoglou