Measuring usefulness of context for context-aware ranking

Abstract

Most of major search engines develop different types of personalisation of search results. Personalisation includes deriving user's long-term preferences, query disambiguation etc. User sessions provide very powerful tool commonly used for these problems. In this paper we focus on personalisation based on context-aware reranking. We implement a machine learning framework to approach this problem and study importance of different types of features. We stress that features concerning temporal and context relatedness of queries along with features relied on user's actions are most important and play crucial role for this type of personalisation.