Machine Learning - Special issue on inductive transfer
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A model of inductive bias learning
Journal of Artificial Intelligence Research
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Directly optimizing an information retrieval (IR) metric has become a hot topic in the field of learning to rank. Conventional wisdom believes that it is better to train for the loss function on which will be used for evaluation. But we often observe different results in reality. For example, directly optimizing averaged precision achieves higher performance than directly optimizing precision@3 when the ranking results are evaluated in terms of precision@3. This motivates us to combine multiple metrics in the process of optimizing IR metrics. For simplicity we study learning with two metrics. Since we usually conduct the learning process in a restricted hypothesis space, e.g., linear hypothesis space, it is usually difficult to maximize both metrics at the same time. To tackle this problem, we propose a relaxed approach in this paper. Specifically, we incorporate one metric within the constraint while maximizing the other one. By restricting the feasible hypothesis space, we can get a more robust ranking model. Empirical results on the benchmark data set LETOR show that the relaxed approach is superior to the direct linear combination approach, and also outperforms other baselines.