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

TL;DR

This paper presents a supervised method to classify web search missions into transactional, navigational, or informational categories using user interaction features, achieving promising accuracy and F1 scores on real-world data.

Contribution

It introduces a novel supervised classification approach leveraging user interaction features to identify search mission intent, enhancing search personalization and targeted services.

Findings

F1 score of 63% and accuracy of 69% on real-world data

High performance (F1>75%) on informational and navigational missions

Potential applications in improving search retrieval and targeted advertising

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.