Abstract

The behaviour, goals, and intentions of users while searching for images in large scale online collections are not well understood, with image search log analysis providing limited insights, in part because they tend only to have access to user \textit{search} and \textit{result click} information. In this paper we study user search behaviour in a large photo-sharing platform, analyzing all user actions during search sessions  (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image search. We show that search behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image search.