This paper proposes an boosting algorithm AdaRank of the issue learning to rank for document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers".
The evaluation of AdaRank is MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain).
Here below is the algorithm of AdaRank
Based on Adaboost, AdaRank is a novel attempt for ranking methods. Although the accuracy of the real experiment is not so good as my expectation, I think the efficiency may be much better than some previous work.
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