Recommender Systems: Basics, Types, and Design Consideration

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

Table of Contents:
00:00 Background
02:32 Introduction and Motivation
09:42 Types of Recommender Systems
14:08 Recommendation Models
40:05 Performance Metrics and its Designs

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