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Under consideration for publication in Knowledge and Information Boosted Ranking Models: A Unifying

Summary: Under consideration for publication in Knowledge and Information
Boosted Ranking Models: A Unifying
Framework for Ranking Predictions
Kevin Dela Rosa 1
, Vangelis Metsis 2
, and Vassilis Athitsos 2
1 Language Technologies Institute, Carnegie Mellon University, Pittsburgh PA, USA
2 Computer Science and Engineering Department, University of Texas at Arlington,
Arlington TX, USA
Abstract. Ranking is an important functionality in a diverse array of applications,
including web search, similarity-based multimedia retrieval, nearest neighbor classifi-
cation, and recommendation systems. In this paper we propose a new method, called
Boosted Ranking Model(BRM), for learning how to rank from training data. An impor-
tant feature of the proposed method is that it is domain-independent, and can thus be
applied to a wide range of ranking domains. The main contribution of the new method
is that it reduces the problem of learning how to rank to the much more simple, and
well-studied, problem of constructing an optimized binary classifier from simple, weak
classifiers. Using that reduction, our method constructs an optimized ranking model us-
ing multiple simple, easy-to-define ranking models as building blocks. The new method


Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington


Collections: Computer Technologies and Information Sciences