  Web search is among the most frequent online activities. Whereas traditional
information retrieval techniques focus on the information need behind a user
query, previous work has shown that user behaviour and interaction can provide
important signals for understanding the underlying intent of a search mission.
An established taxonomy distinguishes between transactional, navigational and
informational search missions, where in particular the latter involve a
learning goal, i.e. the intent to acquire knowledge about a particular topic.
We introduce a supervised approach for classifying online search missions into
either of these categories by utilising a range of features obtained from the
user interactions during an online search mission. Applying our model to a
dataset of real-world query logs, we show that search missions can be
categorised with an average F1 score of 63% and accuracy of 69%, while
performance on informational and navigational missions is particularly
promising (F1>75%). This suggests the potential to utilise such supervised
classification during online search to better facilitate retrieval and ranking
as well as to improve affiliated services, such as targeted online ads.
