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