Research Output
Can we predict QPP? An approach based on multivariate outliers
  Query performance prediction (QPP) aims to predict the success and failure of a search engine on a collection of queries and documents. State of the art predictors can enable this prediction with a degree of accuracy; however, it is far from being perfect. Existing studies have mainly observed QPP is a difficult task but yet have lacked in-depth qualitative analysis. In this paper, we analyze QPP from the perspective of predicting the accuracy of query performance. Our working hypothesis is that certain queries lend themselves more easy to prediction while others pose greater challenges. Moreover, by focusing on outliers, we can pinpoint queries that are particularly difficult to predict. To achieve this, we consider multivariate outlier detection. Our results show the effectiveness of this approach in identifying queries for which QPP struggles to provide accurate predictions. Furthermore, we show that by excluding these difficult to predict queries, the overall accuracy of QPP is substantially improved.


Chifu, A., Déjean, S., Garouani, M., Mothe, J., Ortiz, D., & Ullah, M. Z. (2024). Can we predict QPP? An approach based on multivariate outliers. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part III.



Information Retrieval, Query performance prediction, QPP, Post-retrieval features, Multivariate outlier detection

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