Building Recommendation Systems Using Graph Deep Learning

Recommendation systems have become an integral part of our digital landscape, profoundly shaping how we interact with content, products, and services across various industries. From e-commerce giants such as Amazon to streaming platforms such as Netflix, and social media networks such as Facebook, these systems play a crucial role in enhancing user experience, driving engagement, and boosting business outcomes. As we delve into the world of building recommendation systems using graph deep learning, it’s essential to understand the evolution of these systems and the transformative potential of graph-based approaches.

Traditionally, recommendation systems have relied on techniques such as collaborative filtering (CF), content-based filtering, and hybrid approaches. While these methods have been successful to a certain extent, they often fall short in capturing the complex, interconnected nature of user-item interactions...