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Recommender Systems and its Design


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 will help you understand the basics of recommender systems, the types of models used, and the design considerations for recommender systems. By the end of the session, you will know:
  • About the types of recommender systems
  • About some of the popular Machine-Learning models for recommender systems
  • How to measure the performance of a recommender system
  • The steps to design your own baseline recommender system
  • High-level design considerations for an industry-scale recommender system
  • About a few real-world recommender systems used at different 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. You can also find his lectures on the youtube channel, byte size ml

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