Learning to Rank

Learning to rank studies how to order candidates—such as documents, products, or recommendations—so the most useful items appear first. Approaches include pointwise relevance prediction, pairwise methods such as RankNet and Bradley–Terry models, listwise objectives such as LambdaRank, and neural or LLM-based rerankers; reinforcement learning can also optimize rankings for downstream outcomes. Systems are evaluated with metrics such as NDCG and MRR on retrieval benchmarks and recommendation data, then often tested in online experiments. A central tension is that offline ranking gains may not translate to user or business value, while stronger reranking can add latency and remain vulnerable to bias, position effects, and unstable feedback.