Evaluation Measures in Information Retrieval

Evaluation of information retrieval (IR) systems is critical to making well-informed design decisions. From search to recommendations, evaluation measures are paramount to understanding what does and does not work in retrieval.

Many big tech companies contribute much of their success to well-built IR systems. One of Amazon’s earliest iterations of the technology was reportedly driving more than 35% of their sales[1]. Google attributes 70% of YouTube views to their IR recommender systems[2][3].

This is a companion discussion topic for the original entry at https://www.pinecone.io/learn/offline-evaluation/