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» Learning to rank from a noisy crowd
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WWW
2008
ACM
14 years 10 months ago
Ranking refinement and its application to information retrieval
We consider the problem of ranking refinement, i.e., to improve the accuracy of an existing ranking function with a small set of labeled instances. We are, particularly, intereste...
Rong Jin, Hamed Valizadegan, Hang Li
WWW
2008
ACM
14 years 10 months ago
Finding the right facts in the crowd: factoid question answering over social media
Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, to find out an obscure piece of trivia, it is now ...
Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyua...
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
12 years 7 days ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
SIGIR
2009
ACM
14 years 4 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 9 months ago
Improved answer ranking in social question-answering portals
Community QA portals provide an important resource for non-factoid question-answering. The inherent noisiness of user-generated data makes the identification of high-quality cont...
Felix Hieber, Stefan Riezler