Unsupervised Search Algorithm Configuration using Query Performance Prediction
This work addresses the challenge of search engine configuration for developers, but it appears incremental as it builds on existing query performance prediction techniques.
The paper tackled the problem of search engine configuration for inexpert developers by proposing an unsupervised method using query performance prediction that eliminates the need for relevance labels, demonstrating its merits through two example usecases.
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.