2009 | OriginalPaper | Buchkapitel
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
verfasst von : Pinar Donmez, Jaime G. Carbonell
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user’s preferences or on collaborative filtering. Learning-to-rank is particularly necessary for adaptive or personalizable tasks, including email prioritization, individualized recommendation systems, personalized news clipping services and so on. Whereas the learning-to-rank challenge has been addressed in the literature, little work has been done in an active-learning framework, where requisite user feedback is minimized by selecting only the most informative instances to train the rank learner. This paper addresses active rank-learning head on, proposing a new sampling strategy based on minimizing hinge rank loss, and demonstrating the effectiveness of the active sampling method for rankSVM on two standard rank-learning datasets. The proposed method shows convincing results in optimizing three performance metrics, as well as improvement against four baselines including entropy-based, divergence- based, uncertainty-based and random sampling methods.