JMLR: An Efficient Boosting Algorithm for Combining Preferences

[[Redistributed from JMLR announce]]

~From: "David 'Pablo' Cohn" <[EMAIL PROTECTED]>
~Date: 12 Nov 2003 15:36:54 -0800
~Subject: jmlr-announce: An Efficient Boosting Algorithm for Combining
        Preferences

The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce publication of a new paper:
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An Efficient Boosting Algorithm for Combining Preferences
Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer
JMLR 4(Nov):933-969, 2003

Abstract

We study the problem of learning to accurately rank a set of objects by
combining a given collection of ranking or preference functions. This
problem of combining preferences arises in several applications, such as
that of combining the results of different search engines, or the
"collaborative-filtering" problem of ranking movies for a user based on
the movie rankings provided by other users. In this work, we begin by
presenting a formal framework for this general problem. We then describe
and analyze an efficient algorithm called RankBoost for combining
preferences based on the boosting approach to machine learning. We give
theoretical results describing the algorithm's behavior both on the
training data, and on new test data not seen during training. We also
describe an efficient implementation of the algorithm for a particular
restricted but common case. We next discuss two experiments we carried
out to assess the performance of RankBoost. In the first experiment, we
used the algorithm to combine different web search strategies, each of
which is a query expansion for a given domain. The second experiment is
a collaborative-filtering task for making movie recommendations. 

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This paper, and all previous papers in Volume 4 are available
electronically at http://www.jmlr.org in PostScript and PDF formats. The
papers of Volumes 1, 2 and 3 are also available electronically from the
JMLR website, and in hardcopy from the MIT Press; please see
http://mitpress.mit.edu/JMLR for details.

-David Cohn, <[EMAIL PROTECTED]>

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