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» Smoothing clickthrough data for web search ranking
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ICDE
2008
IEEE
189views Database» more  ICDE 2008»
13 years 11 months ago
Adapting ranking functions to user preference
— Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, whic...
Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, ...
COLING
2010
13 years 8 days ago
Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Machine-learned ranking techniques automatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibilit...
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, ...
CIKM
2010
Springer
13 years 3 months ago
Clickthrough-based translation models for web search: from word models to phrase models
Web search is challenging partly due to the fact that search queries and Web documents use different language styles and vocabularies. This paper provides a quantitative analysis ...
Jianfeng Gao, Xiaodong He, Jian-Yun Nie
CIKM
2008
Springer
13 years 7 months ago
Learning latent semantic relations from clickthrough data for query suggestion
For a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. Due to th...
Hao Ma, Haixuan Yang, Irwin King, Michael R. Lyu
CIKM
2009
Springer
13 years 12 months ago
Exploring relevance for clicks
Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algor...
Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun ...