EntityBot
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Conference article in proceedings
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Date
2021-09-13
Major/Subject
Mcode
Degree programme
Language
en
Pages
4
753-756
753-756
Series
RecSys 2021 - 15th ACM Conference on Recommender Systems
Abstract
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.Description
| openaire: EC/H2020/826266/EU//CO-ADAPT
Keywords
Proactive information retrieval, Real-world tasks, User intent modeling
Other note
Citation
Vuong , T , Andolina , S , Jacucci , G , Daee , P , Klouche , K , Sjöberg , M , Ruotsalo , T & Kaski , S 2021 , EntityBot : Supporting everyday digital tasks with entity recommendations . in RecSys 2021 - 15th ACM Conference on Recommender Systems . ACM , pp. 753-756 , ACM International Conference on Recommender Systems , Virtual, Online , Netherlands , 27/09/2021 . https://doi.org/10.1145/3460231.3478883
