Master Recommendation Engine: Metrics, Trends, and Challenges

Imagine a world where your every online decision is guided seamlessly by technology. Recommender systems are the invisible engines driving this experience, powering 35% of Amazon’s sales and accounting for 80% of Netflix’s viewing hours. With a projected value of over $15 billion by 2030, this domain is more than just a trend—it’s a game-changer reshaping the digital landscape.

In this live session, we’ll dive into the dynamic world of recommendation engines, starting with a comprehensive introduction to the field. We’ll explore the foundational research that has propelled these systems from their early stages to their current advanced forms. Key takeaways from the session include:

• Understand the key performance indicators that measure the success of recommendation algorithms.
• Discover the basics of Content-Based, Collaborative Filtering, and Hybrid approaches.
• Explore the advancements in utilizing implicit data and multi-faceted collaborative filtering.
• Delve into cutting-edge techniques such as Wide and Deep Networks, Two-Tower Networks, and Graph Networks.
• Learn about the present-day challenges in the field and gain practical insights on how to begin your own journey with recommendation engines.