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CIKM
2008
Springer
15 years 3 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
109
Voted
CLEF
2003
Springer
15 years 6 months ago
The CLEF 2003 Interactive Track
The CLEF 2003 Interactive Track (iCLEF) was the third year of a shared experiment design to compare strategies for cross-language search assistance. Two kinds of experiments were p...
Douglas W. Oard, Julio Gonzalo
WWW
2002
ACM
16 years 2 months ago
OCTOPUS: aggressive search of multi-modality data using multifaceted knowledge base
An important trend in Web information processing is the support of multimedia retrieval. However, the most prevailing paradigm for multimedia retrieval, content-based retrieval (C...
Jun Yang 0003, Qing Li, Yueting Zhuang
CIKM
2009
Springer
15 years 2 months ago
Improving search engines using human computation games
Work on evaluating and improving the relevance of web search engines typically use human relevance judgments or clickthrough data. Both these methods look at the problem of learni...
Hao Ma, Raman Chandrasekar, Chris Quirk, Abhishek ...
CIKM
2010
Springer
15 years 3 days ago
Online learning for recency search ranking using real-time user feedback
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohu...