Unsupervised Search Algorithm Configuration using Query Performance Prediction

TL;DR

This paper proposes an unsupervised method for search algorithm configuration that predicts query performance without relevance labels, using only sample queries, thereby simplifying auto-configuration for search engines.

Contribution

It introduces a relevance-label-free approach based on query performance prediction for automatic search configuration.

Findings

Effective in two example use cases

No need for relevance labels or supervised training

Simplifies auto-configuration process

Abstract

Search engine configuration can be quite difficult for inexpert developers. Instead, an auto-configuration approach can be used to speed up development time. Yet, such an automatic process usually requires relevance labels to train a supervised model. In this work, we suggest a simple solution based on query performance prediction that requires no relevance labels but only a sample of queries in a given domain. Using two example usecases we demonstrate the merits of our solution.