Knowledge Graphs for Product Recommendations — Amazon COSMO

As we explored the potential of leveraging knowledge graphs in various applications, we found another use case of real-world usage that we are all very familiar with: e-commerce!

In the vast and ever-evolving world of e-commerce, delivering personalized and relevant product recommendations to customers is a crucial challenge. While traditional recommendation systems often rely on historical purchase data and collaborative filtering, they can struggle to capture the nuanced, commonsense knowledge that underpins many of our everyday purchasing decisions.

Amazon COSMO Framework

That’s where Amazon’s COSMO framework comes into play. COSMO, or the “Common Sense Knowledge Generation and Serving System,” is a groundbreaking approach to building commonsense knowledge graphs that can dramatically improve the performance of product recommendation engines.

At the heart of COSMO is the recognition that commonsense reasoning is essential for understanding the context and relevance of customer queries. If a customer searches for “shoes for pregnant women,” for example, a recommendation system powered by COSMO would understand the implicit need for slip-resistant, comfortable footwear, rather than simply suggesting the most popular or highest-rated shoes.