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» Comparing and Aggregating Rankings with Ties
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CIKM
2008
Springer
13 years 9 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
CVPR
2000
IEEE
14 years 9 months ago
Boosting Image Retrieval
Online photo sharing systems, such as Flickr and Picasa, provide a valuable source of human-annotated photos. Textual annotations are used not only to describe the visual content ...
Kinh Tieu, Paul A. Viola
SIGIR
2009
ACM
14 years 2 months ago
Learning to rank for quantity consensus queries
Web search is increasingly exploiting named entities like persons, places, businesses, addresses and dates. Entity ranking is also of current interest at INEX and TREC. Numerical ...
Somnath Banerjee, Soumen Chakrabarti, Ganesh Ramak...
CIKM
2011
Springer
12 years 7 months ago
Learning to aggregate vertical results into web search results
Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting...
Jaime Arguello, Fernando Diaz, Jamie Callan
ICML
2003
IEEE
14 years 8 months ago
Online Ranking/Collaborative Filtering Using the Perceptron Algorithm
In this paper we present a simple to implement truly online large margin version of the Perceptron ranking (PRank) algorithm, called the OAP-BPM (Online Aggregate Prank-Bayes Poin...
Edward F. Harrington