Predicting Representations of Information Needs from Digital Activity Context

Information retrieval systems often consider search-session and immediately preceding web-browsing history as the context for predicting users’ present information needs. However, such context is only available when a user’s information needs originate from web context or when users have issued preceding queries in the search session. Here, we study the effect of more extensive context information recorded from users’ everyday digital activities by monitoring all information interacted with and communicated using personal computers. Twenty individuals were recruited for 14 days of 24/7 continuous monitoring of their digital activities, including screen contents, clicks, and operating system logs on Web and non-Web applications. Using this data, a transformer architecture is applied to model the digital activity context and predict representations of personalized information needs. Subsequently, the representations of information needs are used for query prediction, query auto-completion, selected search result prediction, and Web search re-ranking. The predictions of the models are evaluated against the ground truth data obtained from the activity recordings. The results reveal that the models accurately predict representations of information needs improving over the conventional search session and web-browsing contexts. The results indicate that the present practice for utilizing users’ contextual information is limited and can be significantly extended to achieve improved search interaction support and performance.