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Dominating ranking algorithm for information retrieval
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010There are lots of ranking algorithms used in Web information retrieval. However, current algorithms have some problems: these algorithms are based on different calculation formulas to calculate the documents and query similarity or train a lot of training data to get corresponding calculation formula which calculate documents and query similarity.
Chen Chen +3 more
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Relevance Vector Ranking for Information Retrieval
Journal of Convergence Information Technology, 2010In recent years, learning ranking function for information retrieval has drawn the attentions of the researchers from information retrieval and machine learning community. In existing approaches of learning to rank, the sparse prediction model only can be learned by support vector learning approach.
Huixia Jin, Xiao Chang, Fengxia Wang
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Learning to rank for geographic information retrieval
Proceedings of the 6th Workshop on Geographic Information Retrieval, 2010The task of Learning to Rank is currently getting increasing attention, providind a sound methodology for combining different sources of evidence. The goal is to design and apply machine learning methods to automatically learn a function from training data that can sort documents according to their relevance.
Pável Calado, Bruno Martins
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Machine Learning Ranking for Structured Information Retrieval
2006We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relevance for a given query, in a collection of structured documents. We propose to improve the performance of a baseline Information Retrieval system by using a learning ranking algorithm which operates on scores computed from ...
Vittaut, Jean-Noël, Gallinari, Patrick
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Learning to rank for biomedical information retrieval
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically.
Bo Xu +6 more
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On rank correlation in information retrieval evaluation
ACM SIGIR Forum, 2007Some methods for rank correlation in evaluation are examined and their relative advantages and disadvantages are discussed. In particular, it is suggested that different test statistics should be used for providing additional information about the experiments other that the one provided by statistical significance testing. Kendall's τ is often used for
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An Overview of Learning to Rank for Information Retrieval
2009 WRI World Congress on Computer Science and Information Engineering, 2009This paper presents an overview of learning to rank. It includes three parts: related concepts including the definitions of ranking and learning to rank; a summary of pointwise models, pairwise models, and listwise models; estimation measures such as Normalized Discount Cumulative Gain and Mean Average Precision, respectively.
Xiaodong Chen +4 more
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A probability ranking principle for interactive information retrieval [PDF]
Information Retrieval, 2008 The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR).
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Ranking refinement and its application to information retrieval [PDF]
Proceedings of the 17th international conference on World Wide Web, 2008 We consider the problem of ranking refinement, i.e., to improve the accuracy of an existing ranking function with a small set of labeled instances. We are, particularly, interested in learning a better ranking function using two complementary sources of information, ranking information given by the existing ranking function (i.e., a base ranker) and ...
Rong Jin, Hamed Valizadegan, Hang Li
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Performance Analysis of Ranking Models in Information Retrieval [PDF]
2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019 Information Retrieval is the process of extracting an appropriate document from a huge dataset in accordance with the user's intention. Retrieval of relevant document tends to be a serious issue in the Information Retrieval system. Ranking models in Information Retrieval system eliminates this difficulty by ranking the documents based on relevancy ...
Selvan G S R Emil +2 more
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