learning to rank
**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.