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» Learning to rank for information retrieval
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ECIR
2009
Springer
14 years 4 months ago
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
Abstract. This paper concerns document ranking in information retrieval. In information retrieval systems, the widely accepted probability ranking principle (PRP) suggests that, fo...
Jun Wang
SIGIR
2011
ACM
12 years 10 months ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
SIGIR
2011
ACM
12 years 10 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
ECIR
1998
Springer
13 years 9 months ago
Independence of Contributing Retrieval Strategies in Data Fusion for Effective Information Retrieval
: In information retrieval, data fusion is a technique for combining the outputs of more than one retrieval strategy which rank documents for retrieval. One of the observations oft...
Alan F. Smeaton
SODA
2007
ACM
145views Algorithms» more  SODA 2007»
13 years 9 months ago
Aggregation of partial rankings, p-ratings and top-m lists
We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-c...
Nir Ailon