In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR.
We devise the first two methods that can utilize historical data
to make feedback available during learning more reliable and
to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system.
Reusing Historical Interaction Data for Faster