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ICDE
2008
IEEE
189views Database» more  ICDE 2008»
14 years 2 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, ...
KDD
2006
ACM
143views Data Mining» more  KDD 2006»
14 years 8 months ago
Mining long-term search history to improve search accuracy
Long-term search history contains rich information about a user's search preferences. In this paper, we study statistical language modeling based methods to mine contextual i...
Bin Tan, Xuehua Shen, ChengXiang Zhai
CORR
2006
Springer
118views Education» more  CORR 2006»
13 years 7 months ago
Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs
Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well kn...
Filip Radlinski, Thorsten Joachims
VLDB
2001
ACM
94views Database» more  VLDB 2001»
14 years 1 days ago
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
In recent years, several methods have been proposed for implementing interactive similarity queries on multimedia databases. Common to all these methods is the idea to exploit use...
Ilaria Bartolini, Paolo Ciaccia, Florian Waas
NIPS
1997
13 years 9 months ago
Learning to Order Things
There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of ...
William W. Cohen, Robert E. Schapire, Yoram Singer