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Discovering Ranking Functions for Information Retrieval
2005The field of information retrieval deals with finding relevant documents from a large document collection or the World Wide Web in response to a user’s query seeking relevant information. Ranking functions play a very important role in the retrieval performance of such retrieval systems and search engines.
Weiguo Fan, Praveen Pathak
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Relevance ranking in Geographical Information Retrieval
SIGSPATIAL Special, 2011The study of relevance is one of the central themes in information science where the concern is to match information objects with expressed information needs of the users. Despite substantial advances in search engines and information retrieval (IR) systems in the past decades, this seemingly intuitive concept of relevance remains to be an illusive one
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Low‐rank Orthogonal Decompositions for Information Retrieval Applications
Numerical Linear Algebra with Applications, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ricardo D. Fierro, Michael W. Berry
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Improving Efficiency and Relevance Ranking in Information Retrieval
IEEE/WIC/ACM International Conference on Web Intelligence (WI'04), 2005The increasing amount of data available on the Internet has made it more important to create efficient IR (Information Retrieval) systems than ever before. However, the execution efficiency of IR tasks under a given scheme is rarely discussed in research.
Carolyn Watters, Lei Dong
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Hybrid Model with Word2vector in Information Retrieval Ranking
2021People have realized the importance of finding and archiving information with the computer advents for thousands of years, and storing of large amount of information became possible. It is actually not related to the fetching of the documents, it informs the user on the whereabouts and existence of the documents.
Nisheeth Joshi +2 more
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A new rank correlation coefficient for information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 2008In the field of information retrieval, one is often faced with the problem of computing the correlation between two ranked lists. The most commonly used statistic that quantifies this correlation is Kendall's Τ. Often times, in the information retrieval community, discrepancies among those items having high rankings are more important than those among ...
Stephen Robertson +2 more
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An outranking approach for rank aggregation in information retrieval
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007Research in Information Retrieval usually shows performanceimprovement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.). In this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided ...
Daniel Vanderpooten, Mohamed Farah
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Efficient Information Retrieval of Encrypted Cloud Data with Ranked Retrieval
2019Over the past decade, there have been massive developments in technology such as self-driving cars, crypto currencies, streaming services, voice assistants etc. In each of the listed breakthroughs, Cloud Computing was involved. Cloud computing has offered a tremendous breakthrough in enterprise and business transformation bringing with it a previously ...
M. Gogul Kumar +2 more
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Learning to rank for information retrieval (LR4IR 2009)
ACM SIGIR Forum, 2009From the experiences of running those two workshops, we have found that there is a community emerging, consisting of people from both academia and industry and including both researchers and practitioners. They have rich experiences of IRand machine learning, and are also deeply interested in the learning to rank technologies.
Tie-Yan Liu, Hang Li, ChengXiang Zhai
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Weighted Rank Correlation in Information Retrieval Evaluation
2009In Information Retrieval (IR), it is common practice to compare the rankings observed during an experiment --- the statistical procedure to compare rankings is called rank correlation. Rank correlation helps decide the success of new systems, models and techniques. To measure rank correlation, the most used coefficient is Kendall's *** . However, in IR,
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