One highlight of YLTR was Chris Burges' MSR team winning track 1 with LambdaMART. They given an overview of their method in a recent tech report:
From RankNet to LambdaRank to LambdaMART: An Overview
LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the recent Yahoo! Learning To Rank Challenge.The other winning teams were from the Russian search company, Yandex. See the company blog post on the topic (via Google translate). You can also read the presentations from the top leaders:
- BagBoo: Bagging the Gradient Boosting by Dmitry Pavlov and Cliff Brunk
- YetiRank: Everybody Lies by Andrey Gulin and Igor Kuralenok
I find YetiRank particularly interesting because they show that modeling error and uncertainty in relevance judgments can improve model effectiveness.