Characterizing and Supporting Cross-Device Search Tasks

Web searchers frequently transition from desktop computers and laptops to mobile devices, and vice versa. Little is known about the nature of cross-device search tasks, yet they represent an important opportunity for search engines to help their users, especially those on the target (post-switch) device. For example, the search engine could save the current session and re-instate it post switch, or it could capitalize on down-time between devices to proactively retrieve content on behalf of the searcher. In this paper, we present a log-based study to define and characterize cross-device search behavior and predict the resumption of cross-device tasks. Using data from a large commercial search engine, we show that there are discernible and noteworthy patterns of search behavior associated with device transitions. We also develop learned models for predicting task resumption on the target device using behavioral, topical, geospatial, and temporal features. Our findings show that our models can attain strong prediction accuracy and have direct implications for the development of tools to help people search more effectively in a multi-device world.