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Learning to rank relational objects and its application to web search
- In WWW ’08
, 2008
"... Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not con ..."
Abstract
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Cited by 12 (5 self)
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Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc., and sometimes it is necessary to utilize the information in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, ‘learning to rank relational objects’. In the new learning
Categories and Subject Descriptors: I.5.3 Clustering: Similarity Measures
"... We present theoretical bounds and empirical robustness of score regularization given changes in the similarity measure. ..."
Abstract
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We present theoretical bounds and empirical robustness of score regularization given changes in the similarity measure.
Categories and Subject Descriptors: H.3.3 Information Search and Retrieval: Relevance Feedback General Terms: Algorithms
"... We demonstrate that regularization can improve feedback in a language modeling framework. ..."
Abstract
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We demonstrate that regularization can improve feedback in a language modeling framework.
BMC Bioinformatics BioMed Central Research article
, 2008
"... This is an Open Access article distributed under the terms of the Creative Commons Attribution License ..."
Abstract
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License
Semi-supervised Learning to Rank with Preference Regularization
"... We propose a semi-supervised learning to rank algorithm. It learns from both labeled data (pairwise preferences or absolute labels) and unlabeled data. The data can consist of multiple groups of items (such as queries), some of which may contain only unlabeled items. We introduce a preference regula ..."
Abstract
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We propose a semi-supervised learning to rank algorithm. It learns from both labeled data (pairwise preferences or absolute labels) and unlabeled data. The data can consist of multiple groups of items (such as queries), some of which may contain only unlabeled items. We introduce a preference regularizer favoring that similar items are similar in preference to each other. The regularizer captures manifold structure in the data, and we also propose a rank-sensitive version designed for top-heavy retrieval metrics including NDCG and mean average precision. The regularizer is employed in SSLambdaRank, a semisupervised version of LambdaRank. This algorithm directly optimizes popular retrieval metrics and improves retrieval accuracy over LambdaRank, a state-of-the-art ranker that was used as part of the winner of the Yahoo! Learning to Rank challenge 2010. The algorithm runs in linear time in the number of queries, and can work with huge datasets.
oro.open.ac.uk A Study of Document Weight Smoothness in Pseudo Relevance Feedback
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oro.open.ac.uk A Study of Document Weight Smoothness in Pseudo Relevance Feedback
, 2010
"... and other research outputs A study of document weight smoothness in pseudo relevance feedback. ..."
Abstract
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and other research outputs A study of document weight smoothness in pseudo relevance feedback.
Content-Based Relevance Estimation on the Web Using Inter-Document Similarities
"... In adversarial and noisy search settings as the Web, the document-query surface level similarity can be a highly misleading relevance signal. Thus, devising content-based relevance estimation (ranking) approaches becomes highly challenging. We address this challenge using two methods that utilize in ..."
Abstract
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In adversarial and noisy search settings as the Web, the document-query surface level similarity can be a highly misleading relevance signal. Thus, devising content-based relevance estimation (ranking) approaches becomes highly challenging. We address this challenge using two methods that utilize inter-document similarities in an initially retrieved list. The first removes documents from the list that exhibit high query similarity, but for which there is insufficient additional support for relevance that is based on interdocument similarities. The method is based on a probabilistic model that decouples document-query similarities from relevance estimation. The second method re-ranks the list by “rewarding ” documents that exhibit high similarity both to the query and to other documents in the list. Both methods incorporate, in addition, at the model level, queryindependent document quality estimates. Extensive empirical evaluation demonstrates the merits of our methods.

