Recommendation systems are everywhere. Since you’re reading this article, there’s a good chance Medium recommended it on your feed. This article will explore NDCG — Normalized Discounted Cumulative Gain, the rank-aware metric for evaluating any recommendation system model.
Recommendation systems help users discover relevant items like products, profiles, posts, videos, ads, or information based on their preferences or behavior. These platforms handle millions of items, and displaying the most relevant ones is key to boosting user engagement and business metrics. Companies such as Amazon, LinkedIn, Twitter, Instagram, Reddit, Spotify, YouTube, Netflix, Medium, and Quora use recommendation systems in their apps.
These systems are typically two-stage systems consisting of a retrieval model followed by a ranking model. The retrieval model funnels down the most relevant items from millions of items based on a similarity metric and passes them to the ranking model. The ranking model ranks the items on a more granular level.