  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.
