The utility of Query Performance Prediction (QPP) methods is commonly evaluated by reporting correlation coefficients to denote how well the methods perform at predicting the retrieval performance of a set of queries. However, a quintessential question remains unexplored: how strong does the correlation need to be in order to realize an increase in retrieval performance? In this work, we address this question in the context of Selective Query Expansion (SQE) and perform a large-scale experiment. The results show that to consistently and predictably improve retrieval effectiveness in the ideal SQE setting, a Kendall's Tau correlation of tau>=0.5 is required, a threshold which most existing query performance prediction methods fail to reach.
|Title of host publication||SIGIR '09 Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Place of Publication||New York, NY, USA|
|Number of pages||2|
|Publication status||Published - 19 Jul 2009|
- query performance prediction