TY - GEN

T1 - EntityBot

T2 - 15th ACM Conference on Recommender Systems, RecSys 2021

AU - Vuong, Tung

AU - Andolina, Salvatore

AU - Jacucci, Giulio

AU - Daee, Pedram

AU - Klouche, Khalil

AU - Sjöberg, Mats

AU - Ruotsalo, Tuukka

AU - Kaski, Samuel

N1 - Funding Information: Partially funded by the EU H2020 project CO-ADAPT, the MIUR (PON AIM), and the Academy of Finland (322653, 328875, 336085, 319264, 292334). Publisher Copyright: © 2021 Owner/Author.

PY - 2021/9/13

Y1 - 2021/9/13

N2 - 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.

AB - 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.

KW - Proactive information retrieval

KW - Real-world tasks

KW - User intent modeling

U2 - 10.1145/3460231.3478883

DO - 10.1145/3460231.3478883

M3 - Article in proceedings

AN - SCOPUS:85115602643

T3 - RecSys 2021 - 15th ACM Conference on Recommender Systems

SP - 753

EP - 756

BT - RecSys 2021 - 15th ACM Conference on Recommender Systems

PB - Association for Computing Machinery, Inc.

Y2 - 27 September 2021 through 1 October 2021

ER -