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Contrasting Neural Click Models and Pointwise IPS Rankers
2023Inverse-propensity scoring and neural click models are two popular methods for learning rankers from user clicks that are affected by position bias. Despite their prevalence, the two methodologies are rarely directly compared on equal footing. In this work, we focus on the pointwise learning setting to compare the theoretical differences of both ...
Philipp Hager +2 more
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Optimizing Base Rankers Using Clicks
2014We study the problem of optimizing an individual base ranker using clicks. Surprisingly, while there has been considerable attention for using clicks to optimize linear combinations of base rankers, the problem of optimizing an individual base ranker using clicks has been ignored.
Anne Schuth +3 more
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Learning a Fast Bipartite Ranker for Text Documents Using Lexicographical Rankers and ROC Curves
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017The design of powerful learning methods for addressing huge amounts of unstructured data, such as text documents, is a fundamental problem within the document analysis and recognition community. In this work, we propose FlexRank, a specially designed bipartite ranking algorithm for text documents using lexicographical ordering. FlexRank is based on the
Lucas de Souza Rodrigues +2 more
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Active query selection for learning rankers
Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception to this (Long et al. 2010) uses expected loss optimization (ELO) to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance ...
Mustafa Bilgic, Paul N. Bennett
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Who Ranks the University Rankers?
Science, 2007Everyone would like to score well in an academic beauty contest. But is it really possible to assess an institution9s worth?
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LEARning Next gEneration Rankers (LEARNER 2017)
Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2017The aim of LEARNER@ICTIR2017 is to investigate new solutions for LtR. In details, we identify some research areas related to LtR which are of actual interest and which have not been fully explored yet. We solicit the submission of position papers on novel LtR algorithms, on evaluation of LtR algorithms, on dataset creation and curation, and on domain ...
Ferro, Nicola +3 more
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This report documents the design and implementation of the Exo-Habitability-Ranker, a web application that ranks known exoplanets by a computed habitability score using the TOPSIS method. We detail data ingestion from the NASA Exoplanet Archive, preprocessing steps including feature selection and normalization, implementation of the TOPSIS algorithm ...
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Clip-Ranker: From Pointwise to Listwise Ranker for Multimodal Retrieval
2022Mengying Xu +3 more
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Multiple Ranker Method in Document Retrieval
2008In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new ...
Dong Li +4 more
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