A significant fraction of information searches are motivated by the user's primary task. An ideal search engine would be able to use information captured from the primary task to proactively retrieve useful information. Previous work has shown that many information retrieval activities depend on the primary task in which the retrieved information is to be used, but fairly little research has been focusing on methods that automatically learn the informational intents from the primary task context.We study howthe implicit primary task context can be used tomodel the user's search intent and to proactively retrieve relevant and useful information. Data comprising of logs from a user study, in which users are writing an essay, demonstrate that users' search intents can be captured from the task and relevant and useful information can be proactively retrieved. Data from simulations with several datasets of different complexity show that the proposed approach of using primary task context generalizes to a variety of data.Our findings have implications for the design of proactive search systems that can infer users' search intent implicitly by monitoring users' primary task activities.
|Journal||ACM Transactions on Interactive Intelligent Systems|
|Publication status||Published - 1 Jul 2018|
|MoE publication type||A1 Journal article-refereed|
- Proactive search
- Task-based information retrieval
- User intent modeling