The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
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Rudin_The P-Norm Push.PDF
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1.09 MB
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Adobe PDF
Checksum (MD5)
7b63d3906517b0a5123ea415c21a174e
Author(s)
Rudin, Cynthia
Date Issued
October 2009
Journal
Journal of Machine Learning Research
Publisher
MIT Press
Citation
Rudin, Cynthia."The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List." Journal of Machine Learning Research 10 (2009) 2233-2271. ©2009 Cynthia Rudin.
Version
Final published version
Abstract
We are interested in supervised ranking algorithms that perform especially well near the top of the
ranked list, and are only required to perform sufficiently well on the rest of the list. In this work,
we provide a general form of convex objective that gives high-scoring examples more importance.
This “push” near the top of the list can be chosen arbitrarily large or small, based on the preference
of the user. We choose ℓp-norms to provide a specific type of push; if the user sets p larger, the
objective concentrates harder on the top of the list. We derive a generalization bound based on
the p-norm objective, working around the natural asymmetry of the problem. We then derive a
boosting-style algorithm for the problem of ranking with a push at the top. The usefulness of the
algorithm is illustrated through experiments on repository data. We prove that the minimizer of the
algorithm’s objective is unique in a specific sense. Furthermore, we illustrate how our objective is
related to quality measurements for information retrieval.
Subjects
information retrieval
ROC
generalization bounds
RankBoost
ranking
MIT Department
Sloan School of Management
Terms of Use
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Persistent DSpace Link
DOI of Published Version
http://www.jmlr.org/papers/volume10/rudin09b/rudin09b.pdf
