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» Learning Models for Ranking Aggregates
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SIGIR
2011
ACM
12 years 8 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
SIGIR
2006
ACM
13 years 11 months ago
Using historical data to enhance rank aggregation
Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless devia...
Miriam Fernández, David Vallet, Pablo Caste...
CIKM
2011
Springer
12 years 5 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
KDD
2009
ACM
245views Data Mining» more  KDD 2009»
14 years 6 months ago
Mining rich session context to improve web search
User browsing information, particularly their non-search related activity, reveals important contextual information on the preferences and the intent of web users. In this paper, ...
Guangyu Zhu, Gilad Mishne
SODA
2007
ACM
145views Algorithms» more  SODA 2007»
13 years 7 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