6533b839fe1ef96bd12a671b
RESEARCH PRODUCT
EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
Giulio JacucciTung VuongMats SjöbergTuukka RuotsaloKhalil KloucheSalvatore AndolinaSamuel KaskiPedram Daeesubject
Settore INF/01 - InformaticaComputer science05 social sciencesWord processingContext (computing)User satisfactionLinear model02 engineering and technologyOptical character recognitionRecommender systemcomputer.software_genreTask (project management)Human–computer interactionUser intent modeling020204 information systems0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesWeb navigationcomputerProactive information retrieval050107 human factorsReal-world tasksdescription
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user’s digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals’ computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then applied to detect the user’s task context to retrieve entities such as applications, documents, contact information, and several keywords determining the task. The system has been evaluated with real-world tasks, demonstrating that the recommendation had an impact on the tasks and led to high user satisfaction.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-09-13 |