## An Efficient Boosting Algorithm for Combining Preferences

**
***Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer*; 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.

[abs][pdf][ps.gz][ps]