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54
Learning diverse rankings with multiarmed bandits
 In Proceedings of the 25 th ICML
, 2008
"... Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We presen ..."
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Cited by 102 (7 self)
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Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users ’ clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worstcase performance even if users’ interests change. 1.
Yahoo! Learning to Rank Challenge Overview
, 2011
"... Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large realworld datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these ..."
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Cited by 72 (6 self)
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Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large realworld datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of stateoftheart learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets.
LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval
"... LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the documen ..."
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Cited by 46 (3 self)
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LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several stateoftheart learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.
Ranking with Ordered Weighted Pairwise Classification
"... In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to ..."
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Cited by 36 (3 self)
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In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to optimize a larger class of loss functions for ranking, based on an ordered weighted average (OWA) (Yager, 1988) of the classification losses. Convex OWA aggregation operators range from the max to the mean depending on their weights, and can be used to focus on the top ranked elements as they give more weight to the largest losses. When aggregating hinge losses, the optimization problem is similar to the SVM for interdependent output spaces. Moreover, we show that OWA aggregates of marginbased classification losses have good generalization properties. Experiments on the Letor 3.0 benchmark dataset for information retrieval validate our approach. 1.
BoltzRank: Learning to Maximize Expected Ranking Gain
"... Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to optimize, as ranking performance is typically judged by metrics that are not smooth. In this paper we propose a new listwi ..."
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Cited by 35 (5 self)
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Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to optimize, as ranking performance is typically judged by metrics that are not smooth. In this paper we propose a new listwise approach to learning to rank. Our method creates a conditional probability distribution over rankings assigned to documents for a given query, which permits gradient ascent optimization of the expected value of some performance measure. The rank probabilities take the form of a Boltzmann distribution, based on an energy function that depends on a scoring function composed of individual and pairwise potentials. Including pairwise potentials is a novel contribution, allowing the model to encode regularities in the relative scores of documents; existing models assign scores at test time based only on individual documents, with no pairwise constraints between documents. Experimental results on the LETOR3.0 data set show that our method outperforms existing learning approaches to ranking. 1.
Fourier Theoretic Probabilistic Inference over Permutations
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2009
"... Permutations are ubiquitous in many realworld problems, such as voting, ranking, and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact and factorized probability distribution representations, such as graphical models, ..."
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Cited by 29 (7 self)
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Permutations are ubiquitous in many realworld problems, such as voting, ranking, and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact and factorized probability distribution representations, such as graphical models, cannot capture the mutual exclusivity constraints associated with permutations. In this paper, we use the “lowfrequency” terms of a Fourier decomposition to represent distributions over permutations compactly. We present Kronecker conditioning, a novel approach for maintaining and updating these distributions directly in the Fourier domain, allowing for polynomial time bandlimited approximations. Low order Fourierbased approximations, however, may lead to functions that do not correspond to valid distributions. To address this problem, we present a quadratic program defined directly in the Fourier domain for projecting the approximation onto a relaxation of the polytope of legal marginal distributions. We demonstrate the effectiveness of our approach on a real camerabased multiperson tracking scenario.
Learning to Rank by Optimizing NDCG Measure
"... Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels. The ranking algorithms are often evaluated using information retrieval measures, such as Normalized Discounted Cumulative Gain (NDCG) [1] and Mean Average Precis ..."
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Cited by 27 (4 self)
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Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels. The ranking algorithms are often evaluated using information retrieval measures, such as Normalized Discounted Cumulative Gain (NDCG) [1] and Mean Average Precision (MAP) [2]. Until recently, most learning to rank algorithms were not using a loss function related to the above mentioned evaluation measures. The main difficulty in direct optimization of these measures is that they depend on the ranks of documents, not the numerical values output by the ranking function. We propose a probabilistic framework that addresses this challenge by optimizing the expectation of NDCG over all the possible permutations of documents. A relaxation strategy is used to approximate the average of NDCG over the space of permutation, and a bound optimization approach is proposed to make the computation efficient. Extensive experiments show that the proposed algorithm outperforms stateoftheart ranking algorithms on several benchmark data sets. 1
Parallel Boosted Regression Trees for Web Search Ranking
"... Gradient Boosted Regression Trees (GBRT) are the current stateoftheart learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. In this paper, we propose a novel method for parallelizing the training of GBRT. Our technique parallelizes the constructio ..."
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Cited by 25 (2 self)
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Gradient Boosted Regression Trees (GBRT) are the current stateoftheart learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. In this paper, we propose a novel method for parallelizing the training of GBRT. Our technique parallelizes the construction of the individual regression trees and operates using the masterworker paradigm as follows. The data are partitioned among the workers. At each iteration, the worker summarizes its datapartition using histograms. The master processor uses these to build one layer of a regression tree, and then sends this layer to the workers, allowing the workers to build histograms for the next layer. Our algorithm carefully orchestrates overlap between communication and computation to achieve good performance.
Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval
 INF RETRIEVAL
, 2012
"... ..."
Generating labels from clicks
 In WSDM
, 2009
"... The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who assigns a score of Perfect, Excellent, etc., depending on how well the URL matches the query. In this paper, we study wh ..."
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Cited by 16 (6 self)
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The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who assigns a score of Perfect, Excellent, etc., depending on how well the URL matches the query. In this paper, we study whether clicks can be used to automatically generate good labels. Intuitively, documents that are clicked (resp., skipped) in aggregate can indicate relevance (resp., lack of relevance). We give a novel way of transforming clicks into weighted, directed graphs inspired by eyetracking studies and then devise an objective function for finding cuts in these graphs that induce a good labeling. In its full generality, the problem is NPhard, but we show that, in the case of two labels, an optimum labeling can be found in linear time. For the more general case, we propose heuristic solutions. Experiments on real click logs show that clickbased labels align with the opinion of a panel of judges, especially as the consensus of the panel grows stronger.