Active Sampling for Rank Learning via Optimizing the Area Under the ROC Curve Pinar Donmez Jaime G. Carbonell 10.1184/R1/6602975.v1 https://kilthub.cmu.edu/articles/journal_contribution/Active_Sampling_for_Rank_Learning_via_Optimizing_the_Area_Under_the_ROC_Curve/6602975 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 entropbased, divergence-based, uncertainty-based and random sampling methods. 2012-10-01 00:00:00 Active learning document retrieval rank learning AUC hinge loss performance optimization