Learning to rank (LTR) uses machine learning to optimize ranking — training models to order items by relevance, popularity, or other objectives, fundamental to search engines, recommender systems, and any application requiring ordered results.

What Is Learning to Rank?

• Definition: ML approaches to ranking items.
• Input: Query/user + candidate items + features.
• Output: Ranked list of items.
• Goal: Learn optimal ranking function from data.

LTR Approaches

Pointwise: Predict relevance score for each item independently, then sort. Pairwise: Learn which item should rank higher in pairs. Listwise: Optimize entire ranked list directly.

Why LTR?

• Complexity: Ranking involves many features, complex interactions.
• Data-Driven: Learn from user behavior (clicks, purchases).
• Optimization: Directly optimize ranking metrics (NDCG, MRR).
• Personalization: Learn user-specific ranking functions.

Applications: Search engines (Google, Bing), e-commerce (Amazon), recommender systems (Netflix, Spotify), ad ranking, job search.

Algorithms: RankNet, LambdaMART, LambdaRank, ListNet, XGBoost, LightGBM, neural ranking models.

Features: Query-document relevance, popularity, freshness, user preferences, context.

Evaluation: NDCG, MAP, MRR, precision@K, click-through rate.

Tools: XGBoost, LightGBM, TensorFlow Ranking, RankLib, scikit-learn.

Learning to rank is the foundation of modern search and recommendations — by learning optimal ranking functions from data, LTR enables personalized, relevant, and engaging ordered results across countless applications.