Abstract
Query-Performance Prediction (QPP) is currently primarily applied to ad-hoc retrieval tasks. The Information Retrieval (IR) field is reaching new heights thanks to recent advances in large language models and neural networks, as well as emerging new ways of searching, such as conversational search. Such advancements are quickly spreading to adjacent research areas, including QPP, necessitating a reconsideration of how we perform and evaluate QPP. This workshop sought to elicit discussion on three topics related to the future of QPP: exploiting advances in IR to improve QPP, instantiating QPP on new search paradigms, and evaluating QPP on new tasks.
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Faggioli, G., Ferro, N., Mothe, J., Raiber, F. (2023). QPP++ 2023: Query-Performance Prediction and Its Evaluation in New Tasks. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_42
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DOI: https://doi.org/10.1007/978-3-031-28241-6_42
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