Summary: Under consideration for publication in Knowledge and Information
Systems
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, similarity­based 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 domain­independent, 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
well­studied, 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, easy­to­define ranking models as building blocks. The new method
is a unifying framework that includes, as special cases, specific methods that we have
proposed in earlier publications for specific ranking applications, such as nearest neigh­

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

Collections: Computer Technologies and Information Sciences