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» Evaluating the Robustness of Learning from Implicit Feedback
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PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
13 years 6 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
CEAS
2008
Springer
13 years 9 months ago
Filtering Email Spam in the Presence of Noisy User Feedback
Recent email spam filtering evaluations, such as those conducted at TREC, have shown that near-perfect filtering results are attained with a variety of machine learning methods wh...
D. Sculley, Gordon V. Cormack
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
SIGIR
2011
ACM
12 years 10 months ago
Active learning to maximize accuracy vs. effort in interactive information retrieval
We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
Aibo Tian, Matthew Lease
IRI
2007
IEEE
14 years 1 months ago
Automated Multimedia Systems Training Using Association Rule Mining
User feedback is widely deployed in recent multimedia research to refine retrieval performance. However, most of the existing online learning algorithms handle interactions of a s...
Na Zhao, Shu-Ching Chen, Stuart Harvey Rubin