# Unsupervised Search Algorithm Configuration using Query Performance Prediction

arXiv: https://arxiv.org/abs/2210.00767 · HTML: https://mlsanity.com/paper/2210.00767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Efficient and Effective Tail Latency Minimization in Multi-Stage Retrieval Systems](https://mlsanity.com/paper/1704.03970/) (2017)

## Abstract (arXiv)

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.