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Recommender Systems and its Design (Part 2)


Recommender systems have a wide range of applications in the industry with movie, music and product recommendations across top tech companies like Netflix, Spotify, Amazon, etc.

Consumers on the web are increasingly relying on recommendations to purchase the next product on Amazon or watch the next YouTube or TikTok video or read the next post on LinkedIn. In short, recommendation systems make life easier by proactively surfacing content for consumers to consume, thus saving time and also increasing customer satisfaction.

This talk is a continuation of the previous talk (part 1), which introduced recommender systems (watch it here).

In Part 2, we will be diving deeper into deep-learning models, architectures, and design considerations for recommender systems. By the end of the session, you will know:

– About Deep learning models for recommender systems
– Popular architectures for recommendation systems
– Deep-dive into design considerations for recommender systems
– How to design your own recommender system
– Recommender systems at Top Tech Companies

Karthik Mohan
Dr. Karthik Mohan

Affiliate Professor at the University of Washington

Dr. Karthik Mohan is currently an affiliate professor at the University of Washington, Seattle, and has in the past worked as a Senior Applied Scientist at Meta and Amazon. He has had the opportunity to work on co-building one of the first neural-network-based recommender systems at Amazon and one of the first automated Natural Language Generation systems at Meta. Dr. Mohan has also taught a wide variety of cutting-edge machine-learning courses at Udub including Recommender systems and Computer Vision to name a few.

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