An Efficient Boosting Algorithm for Combining Preferences

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An Efficient Boosting Algorithm for Combining Preferences (1999)

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by Raj Dharmarajan Iyer , Jr.

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727 - 18 self

BibTeX

@MISC{Iyer99anefficient,
    author = {Raj Dharmarajan Iyer and Jr.},
    title = {An Efficient Boosting Algorithm for Combining Preferences},
    year = {1999}
}

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Abstract

The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing RankBoost to nearest-neighbor and regression algorithms.

Keyphrases

combining preference efficient boosting algorithm multiple preference different search engine second experiment several application efficient implementation efficient algorithm collaborative-filtering task first experiment regression algorithm certain natural case different www search strategy movie recommendation new boosting algorithm individual search strategy query expansion formal framework present result

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