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26
Letor: Benchmark dataset for research on learning to rank for information retrieval
- In Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval
, 2007
"... This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that ..."
Abstract
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Cited by 156 (16 self)
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we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from
Selection bias in the LETOR datasets
"... The LETOR datasets consist of data extracted from traditional IR test corpora. For each of a number of test topics, a set of documents has been extracted, in the form of features of each document-query pair, for use by a ranker. An examination of the ways in which documents were selected for each to ..."
Abstract
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Cited by 12 (0 self)
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The LETOR datasets consist of data extracted from traditional IR test corpora. For each of a number of test topics, a set of documents has been extracted, in the form of features of each document-query pair, for use by a ranker. An examination of the ways in which documents were selected for each
A Test Collection of Preference Judgments
"... We describe an initial release of a set of binary preference judgments over a subset of the LETOR data. These judgments are meant to serve as a starting point for research into questions of evaluation and learning over non-binary, multi-item assessments. 1. ..."
Abstract
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Cited by 5 (0 self)
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We describe an initial release of a set of binary preference judgments over a subset of the LETOR data. These judgments are meant to serve as a starting point for research into questions of evaluation and learning over non-binary, multi-item assessments. 1.
Combination of Documents Features Based on Simulated Click-through Data
"... Abstract. Many different ranking algorithms based on content and context have been used in web search engines to find pages based on a user query. Furthermore, to achieve better performance some new solutions combine different algorithms. In this paper we use simulated click-through data to learn ho ..."
Abstract
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Cited by 1 (0 self)
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how to combine many content and context features of web pages. This method is simple and practical to use with actual click-through data in a live search engine. The proposed approach is evaluated using the LETOR benchmark and we found it is competitive to Ranking SVM based on user judgments. Keywords
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 ..."
Abstract
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Cited by 35 (5 self)
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only on individual documents, with no pairwise constraints between documents. Experimental results on the LETOR3.0 data set show that our method out-performs existing learning approaches to ranking. 1.
Structured learning for non-smooth ranking losses
- In SIGKDD Conference
, 2008
"... Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking cr ..."
Abstract
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Cited by 35 (2 self)
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over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.
Learning to rank with pairwise regularized least-squares
- SIGIR 2007 Workshop on Learning to Rank for Information Retrieval
, 2007
"... Learning preference relations between objects of interest is one of the key problems in machine learning. Our approach for addressing this task is based on pairwise comparisons for estimation of overall ranking. In this paper, we propose a simple preference learning algorithm based on regularized le ..."
Abstract
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Cited by 19 (10 self)
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regularized least-squares regression, despite the fact that the number of training data point pairs under consideration grows quadratically with respect to the number of individual points. As a representative example of a case where the data points outnumber features we choose the Letor dataset
A Co-Ranking Algorithm for Learning Listwise Ranking Functions from Unlabeled Data
"... Abstract—In this paper, we propose a co-ranking algorithm that trains listwise ranking functions using unlabeled data simultaneously with a small number of labeled data. The coranking algorithm is based on the co-training paradigm that is a very common scheme in the semi-supervised classification fr ..."
Abstract
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of base ranker begins to decrease on validation set. In this method, we assume that the unlabeled data follows the same generative distribution as the labeled data. The effectiveness of the presented co-ranking algorithm is demonstrated by experimental results on the benchmark datasets LETOR. Index Terms
Document Selection Methodologies for Efficient and Effective Learning-to-Rank
, 2009
"... Learning-to-rank has attracted great attention in the IR community. Much thought and research has been placed on query-document feature extraction and development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on selecting documents for learning- ..."
Abstract
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Cited by 14 (2 self)
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-to-rank data sets nor on the effect of these choices on the efficiency and effectiveness of learning-to-rank algorithms. In this paper, we employ a number of document selection methodologies, widely used in the context of evaluation – depth-k pooling, sampling (infAP, statAP), active-learning (MTC), and on
Algorithms, Experimentation
"... Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorithms has focused on cases where only labeled data is available for training (i.e. supervised learning). In this paper, we c ..."
Abstract
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Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorithms has focused on cases where only labeled data is available for training (i.e. supervised learning). In this paper, we
Results 1 - 10
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26
