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.

翻译：个性化通常能提升查询性能，但在少数情况下也可能对其造成损害。若能预测此类情形并禁用个性化，则可提升整体性能，使用户对个性化系统的满意度更高。我们采用若干前沿的检索前查询性能预测器，并基于前述目标引入结合用户画像信息的其他预测器。研究这些预测器与个性化查询及原始查询差异之间的相关性，同时运用分类与回归技术优化结果，最终达到最大理想性能的三分之一以上。我们认为这在该研究方向上是一个良好起点，但仍需进一步研究与改进。