Rule-Based Active Sampling for Learning to Rank
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Learning to rank (L2R) algorithms rely on a labeled training set to generate a ranking model that can be later used to rank new query results. Producing these labeled training sets is usually very costly as it requires human annotators to assess the relevance or order the elements in the training set. Recently, active learning alternatives have been proposed to reduce the labeling effort by selectively sampling an unlabeled set. In this paper we propose a novel rule-based active sampling method for Learning to Rank. Our method actively samples an unlabeled set, selecting new documents to be labeled based on how many relevance inference rules they generate given the previously selected and labeled examples. The smaller the number of generated rules, the more dissimilar and more "informative" is a document with regard to the current state of the labeled set. Differently from previous solutions, our algorithm does not rely on an initial training seed and can be directly applied to an unlabeled dataset. Also in contrast to previous work, we have a clear stop criterion and do not need to empirically discover the best configuration by running a number of iterations on the validation or test sets. These characteristics make our algorithm highly practical. We demonstrate the effectiveness of our active sampling method on several benchmarking datasets, showing that a significant reduction in training size is possible. Our method selects as little as 1.1% and at most 2.2% of the original training sets, while providing competitive results when compared to state-of-the-art supervised L2R algorithms that use the complete training sets.
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