Learning to Order Things

TL;DR

This paper introduces a two-stage method for learning to order instances based on preference judgments, using an online algorithm and greedy approximation, with applications to metasearch and search expert combination.

Contribution

It presents a novel two-stage approach combining online preference learning with greedy algorithms for ordering, and applies it to metasearch scenarios.

Findings

The online preference learning algorithm effectively captures ordering preferences.

Greedy algorithms provide good approximations for ordering problems.

Experimental results demonstrate the approach's effectiveness in web search applications.

Abstract

There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's 'Hedge' algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of 'search experts', each of which is a domain-specific query expansion strategy for a web search engine.