Session-based query performance prediction
Andrey Kustarev, Yury Ustinovskiy, Anna Mazur, Pavel Serdyukov · 2012
Search sessions are known to be a rich source of diverse valuable information for individual query analysis. In this paper, we address the problem of query performance prediction by utilizing the entire logical search sessions containing the given query. Guided by the intuitions based on the observations made after the analysis of the search sessions' properties and performance of the queries they contain, we propose a number of features that significantly advance the existing query performance prediction models. Some of them specifically allow to focus on tail queries with sparse click-through statistics.