A collection of public works on Learning to Rank from Microsoft

Nowadays, Learning to Rank is a quite popular subject. Search engines are no longer using only convential Random Walks on web graphs (e.g. PageRank and similar algos), but they learn from past user queries and clicks. This is a collection of recent public works @ Microsoft:

• RankNet [2005]
• LambdaRank [2006-2009], works directly on optimizing DCG
• BoostTreeRank [2006], ranking as a classification problem and uses boosting
• LamdbaMart [2009], which combines the above with boosting, regression trees and allows to have submodels