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arXiv · 2207.01256

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

Ran Yu·Limock·Stefan Dietze
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Abstract

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.

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Ran Yu, Limock, Stefan Dietze. 2022-07-04. Still Haven't Found What You're Looking For -- Detecting the Intent of Web Search Missions from User Interaction Features. https://arxiv.org/abs/2207.01256

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