Recommendation systems challenges at Twitter scale
Who is this presentation for?
- ML engineers, data scientists, engineering managers, and software engineers
Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Many traditional recommendation systems approaches don’t work directly at the scale.
Ashish Bansal provides a window into the world of fast-moving recommendations at massive scale and shares practical ideas on why this is such a fun problem to work on.
- Familiarity with machine learning and software engineering
- Familiarity with NLP and recommendation systems like collaborative filtering (useful but not required)
What you'll learn
- Understand how to balance scalability with complexity of algorithms and how to build a RecSys with catalog of 100x Amazon's catalog
Ashish Bansal is a senior engineering manager, recommendations, leading recommendations teams working on events and trends at Twitter. He focuses on building scalable ML and recommendation systems. Previously, he was a senior director of data science at Capital One where he used AI/ML to generate insights from vast amounts of data and build interesting B2B, B2C, and enterprise products; he cofounded GALE Partners and headed the Machine Learning Group, building AI/ML based marketing automation products. He helped the company grow from 9 to 120 in two years and set up the India office. He has over 19 years of experience in the technology industry, an MBA from the Kellogg School of Management at Northwestern University and a BTech from IIT BHU.
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