Abstract #

Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents. While state-of-the-art predictors offer a certain level of precision, their accuracy is not flawless. Prior research has recognized the challenges inherent in QPP but often lacks a thorough qualitative analysis. In this paper, we delve into QPP by examining the factors that influence the predictability of query performance accuracy. We propose the working hypothesis that while some queries are readily predictable, others present significant challenges. By focusing on outliers, we aim to identify the queries that are particularly challenging to predict. To this end, we employ multivariate outlier detection method. Our results demonstrate the effectiveness of this approach in identifying queries on which QPP do not perform well, yielding less reliable predictions. Moreover, we provide evidence that excluding these hard-to-predict queries from the analysis significantly enhances the overall accuracy of QPP.

Citation #

@inbook{Garouani_2024_ECIR,
 author = {Chifu, Adrian-Gabriel and Déjean, Sébastien and Garouani, Moncef and Mothe, Josiane and Ortiz, Diégo and Ullah, Md Zia},
 booktitle = {Advances in Information Retrieval},
 doi = {10.1007/978-3-031-56063-7_38},
 isbn = {9783031560637},
 issn = {1611-3349},
 pages = {458–467},
 publisher = {Springer Nature Switzerland},
 title = {Can We Predict QPP? An Approach Based on Multivariate Outliers},
 year = {2024}
}