Definition
Unlike standard classification or regression, learning to rank focuses on predicting a relative ordering of items. It uses pairwise, listwise, or pointwise approaches to minimize ranking errors like NDCG or MAP. This technique is essential for information retrieval systems, recommendation engines, and ad placement, where the goal is to present the most relevant results at the top of a list rather than just predicting individual labels.
Summary
Learning to rank is a supervised machine learning technique used to order items by their relevance to a given query, commonly used in search engines.
Key Concepts
- NDCG
- Pairwise ranking
- Information retrieval
Use Cases
- Search engine result ordering
- Product recommendation systems
- Ad ranking algorithms