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KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 8 months ago
Query chains: learning to rank from implicit feedback
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
Filip Radlinski, Thorsten Joachims
COLING
2010
13 years 2 months 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, ...
KDD
2006
ACM
198views Data Mining» more  KDD 2006»
14 years 8 months ago
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose t...
Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei...
WWW
2011
ACM
13 years 2 months ago
Identifying primary content from web pages and its application to web search ranking
Web pages are usually highly structured documents. In some documents, content with different functionality is laid out in blocks, some merely supporting the main discourse. In ot...
Srinivas Vadrevu, Emre Velipasaoglu
WISE
2005
Springer
14 years 1 months ago
Temporal Ranking of Search Engine Results
Existing search engines contain the picture of the Web from the past and their ranking algorithms are based on data crawled some time ago. However, a user requires not only relevan...
Adam Jatowt, Yukiko Kawai, Katsumi Tanaka