Learning to Rank using Gradient Descent, Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender, 2005Proceedings of the 22nd International Conference on Machine Learning (ACM)DOI: 10.1145/1102351.1102363 - This paper introduces RankNet, which forms the basis for pairwise ranking and the concept of lambda gradients, foundational for subsequent advancements.
From RankNet to LambdaRank to LambdaMART: An Overview, Chris J.C. Burges, 2010Microsoft Research Technical Report MSR-TR-2010-82 (Microsoft Research) - Provides a comprehensive overview of the evolution and underlying principles of LambdaRank and LambdaMART, essential for understanding their mechanisms.
Learning to Rank for Information Retrieval, Tie-Yan Liu, 2009Foundations and Trends in Information Retrieval, Vol. 3 (Now Publishers)DOI: 10.1561/1500000016 - A foundational survey providing a broad context for Learning to Rank, including various pairwise and listwise approaches and their motivations.