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» Aggregating User-Centered Rankings to Improve Web Search
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ECAI
2010
Springer
13 years 7 months ago
Learning Aggregation Functions for Expert Search
Abstract. Machine learning techniques are increasingly being applied to problems in the domain of information retrieval and text mining. In this paper we present an application of ...
Ronan Cummins, Mounia Lalmas, Colm O'Riordan
CIKM
2010
Springer
13 years 5 months ago
Exploiting site-level information to improve web search
Ranking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranking that relies on exogenous r...
Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josif...
EMNLP
2009
13 years 4 months ago
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The res...
Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie...
SAC
2006
ACM
13 years 6 months ago
Undue influence: eliminating the impact of link plagiarism on web search rankings
Link farm spam and replicated pages can greatly deteriorate link-based ranking algorithms like HITS. In order to identify and neutralize link farm spam and replicated pages, we lo...
Baoning Wu, Brian D. Davison
SIGIR
2009
ACM
14 years 1 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...