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» Maximum Margin Ranking Algorithms for Information Retrieval
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CIKM
2010
Springer
13 years 6 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
SIGIR
2009
ACM
14 years 2 months ago
Smoothing clickthrough data for web search ranking
Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web sea...
Jianfeng Gao, Wei Yuan, Xiao Li, Kefeng Deng, Jian...
WWW
2008
ACM
14 years 8 months ago
Larger is better: seed selection in link-based anti-spamming algorithms
Seed selection is of significant importance for the biased PageRank algorithms such as TrustRank to combat link spamming. Previous work usually uses a small seed set, which has a ...
Qiancheng Jiang, Lei Zhang, Yizhen Zhu, Yan Zhang
WWW
2004
ACM
14 years 8 months ago
Distributed ranking over peer-to-peer networks
Query flooding is a problem existing in Peer-to-Peer networks like Gnutella. Firework Query Model solves this problem by Peer Clustering and routes the query message more intellig...
Dexter Chi Wai Siu, Tak Pang Lau
WWW
2004
ACM
14 years 8 months ago
Ranking the web frontier
The celebrated PageRank algorithm has proved to be a very effective paradigm for ranking results of web search algorithms. In this paper we refine this basic paradigm to take into...
Nadav Eiron, Kevin S. McCurley, John A. Tomlin