Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve

Abstract. 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, a...

• Type: Conference
• Year: 2009
• Where: ECIR
• Authors: Pinar Donmez, Jaime G. Carbonell
• Keywords: Active Sampling Method, Computer Science, ECIR 2009, Hinge Rank Loss, Random Sampling Methods