# LambdaRank - Key observations of LambdaRank: - To train a model, we do not need the costs themselves, only the gradients (of the costs. wrt model scores). - The gradient should be bigger for pairs of documents that produces a bigger impact in NDCG by swapping positions. LambdaRank [Burges et al., 2006] Multiply actual gradients with the change in NDCG by swapping the rank positions of the two documents: $ \lambda_{\text {LambdaRank }}=\lambda_{\text {RankNet }} \cdot|\Delta \mathrm{NDCG}| $ - This approach also works with other metrics, e.g. $\mid \Delta$ Precision| Empirically LambdaRank was shown to directly optimize IR metrics. - Recently, it was theoretically proven that LambdaRank optimizes a lower bound on certain IR metrics [Wang et al., 2018 ] --- ## References