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» Learning to rank for information retrieval (LR4IR 2008)
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SIGIR
2011
ACM
14 years 9 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
141
Voted
CORR
2008
Springer
114views Education» more  CORR 2008»
15 years 6 months ago
A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables
The LETOR website contains three information retrieval datasets used as a benchmark for testing machine learning ideas for ranking. Algorithms participating in the challenge are re...
Nir Ailon
ADMA
2008
Springer
151views Data Mining» more  ADMA 2008»
16 years 14 days ago
Link-Contexts for Ranking
Anchor text has been shown to be effective in ranking[6] and a variety of information retrieval tasks on web pages. Some authors have expanded on anchor text by using the words ar...
Jessica Gronski
IPM
2008
78views more  IPM 2008»
15 years 6 months ago
Looking back: On relevance, probabilistic indexing and information retrieval
Forty-eight years ago Maron and Kuhns published their paper, ``On Relevance, Probabilistic Indexing and Information Retrieval" (1960). This was the first paper to present a p...
Paul Thompson
CIKM
2008
Springer
15 years 8 months ago
Ranked feature fusion models for ad hoc retrieval
We introduce the Ranked Feature Fusion framework for information retrieval system design. Typical information retrieval formalisms such as the vector space model, the bestmatch mo...
Jeremy Pickens, Gene Golovchinsky