Ranking Measures and Loss Functions in Learning to Rank

@inproceedings{Chen2009RankingMA,
  title={Ranking Measures and Loss Functions in Learning to Rank},
  author={Wei Chen and Tie-Yan Liu and Yanyan Lan and Zhiming Ma and Hang Li},
  booktitle={NIPS},
  year={2009}
}
Learning to rank has become an important research topic in machine learning. While most learning-to-rank methods learn the ranking functions by minimizing loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking functions. In this work, we reveal the relationship between ranking measures and loss functions in learningto-rank methods, such as Ranking SVM, RankBoost, RankNet, and ListMLE. We show that the loss functions of… CONTINUE READING
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Essential loss: Bridge the gap between ranking measures and loss functions in learning to rank

  • W. Chen, T.-Y. Liu, Y. Lan, Z. Ma, H. Li
  • Technical report, Microsoft Research, MSR- TR…
  • 2009
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