Predicting IR Personalization Performance using Pre-retrieval Query Predictors Vicente-López, Eduardo Campos Ibáñez, Luis Miguel Fernández Luna, Juan Manuel Huete, Juan F. Information retrieval Personalization Query difficulty Performance prediction Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements. 2021-03-26T12:12:28Z 2021-03-26T12:12:28Z 2018-01-30 info:eu-repo/semantics/article Journal of Intelligent Information Systems volume 51, pages 597–620(2018) http://hdl.handle.net/10481/67740 10.1007/s10844-018-0498-3 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Springer