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Web search is among the most frequent online activities. Whereas traditionalinformation retrieval techniques focus on the information need behind a userquery, previous work has shown that user behaviour and interaction can provideimportant signals for understanding the underlying intent of a search mission.An established taxonomy distinguishes between transactional, navigational andinformational search missions, where in particular the latter involve alearning goal, i.e. the intent to acquire knowledge about a particular topic.We introduce a supervised approach for classifying online search missions intoeither of these categories by utilising a range of features obtained from theuser interactions during an online search mission. Applying our model to adataset of real-world query logs, we show that search missions can becategorised with an average F1 score of 63% and accuracy of 69%, whileperformance on informational and navigational missions is particularlypromising (F1>75%). This suggests the potential to utilise such supervisedclassification during online search to better facilitate retrieval and rankingas well as to improve affiliated services, such as targeted online ads.

Still Haven't Found What You're Looking For -- Detecting the Intent of Web Search Missions from User Interaction Features