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Machine learned ranking functions have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, the authors have seen a big bottleneck, i.e., the problem of insufficient labeled training data, which has significantly slowed the development and deployment of machine learned ranking functions for different domains. There are two possible approaches to address this problem: combining labeled training data from similar domains with the small target-domain labeled data for training or using pairwise preference data extracted from user click through log for the target domain for training.
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