Evaluation Metrics for Search and Recommendation Systems

This article provides an overview of commonly used evaluation metrics in search and recommendation systems, including precision@K, recall@K, MAP@K, MRR@K, and NDCG@K. These metrics can be categorized into not rank-aware vs. rank-aware metrics, with the latter considering both the number of relevant items and their position in the list of results. The article also demonstrates how to calculate each metric using Python's pytrec_eval library and provides a minimal example dataset for illustration purposes.