Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve. Donmez, P. & Carbonell, J. G. In Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, of ECIR '09, pages 78–89, Toulouse, France, April, 2009. Springer-Verlag. ![Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{donmez_active_2009,
address = {Toulouse, France},
series = {{ECIR} '09},
title = {Active {Sampling} for {Rank} {Learning} via {Optimizing} the {Area} under the {ROC} {Curve}},
isbn = {978-3-642-00957-0},
url = {https://doi.org/10.1007/978-3-642-00958-7_10},
doi = {10.1007/978-3-642-00958-7_10},
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, as well as improvement against four baselines including entropy-based, divergence- based, uncertainty-based and random sampling methods.},
urldate = {2020-04-08},
booktitle = {Proceedings of the 31th {European} {Conference} on {IR} {Research} on {Advances} in {Information} {Retrieval}},
publisher = {Springer-Verlag},
author = {Donmez, Pinar and Carbonell, Jaime G.},
month = apr,
year = {2009},
keywords = {AUC, Active learning, document retrieval, hinge loss, performance optimization, rank learning},
pages = {78--89}
}
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