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Rademacher and gaussian complexities: risk bounds and structural results
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Query chains: learning to rank from implicit feedback
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Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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Topical link analysis for web search
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Regularized estimation of mixture models for robust pseudo-relevance feedback
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Listwise approach to learning to rank: theory and algorithm
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Directly optimizing evaluation measures in learning to rank
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Query dependent ranking using K-nearest neighbor
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Learning to rank with partially-labeled data
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Learning to rank with SoftRank and Gaussian processes
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Learning to rank at query-time using association rules
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Evaluation over thousands of queries
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Structured learning for non-smooth ranking losses
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Generalization analysis of listwise learning-to-rank algorithms
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Subset ranking using regression
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Stability and generalization of bipartite ranking algorithms
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Learning to rank with a novel kernel perceptron method
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Learning concept importance using a weighted dependence model
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Learning to rank for geographic information retrieval
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The anatomy of an ad: structured indexing and retrieval for sponsored search
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Hierarchical feature selection for ranking
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Mining Query Logs: Turning Search Usage Data into Knowledge
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Mixture model based contextual image retrieval
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Learning to rank only using training data from related domain
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Connecting with the collective: self-contained reranking for collaborative recommendation
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On the suitability of diversity metrics for learning-to-rank for diversity
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Information Retrieval
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Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.