EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
Tung Vuong,Salvatore Andolina,Giulio Jacucci,Pedram Daee,Khalil Klouche,Mats Sjöberg,Tuukka Ruotsalo,Samuel Kaski +7 more
- 13 Sep 2021
- pp 753-756
TL;DR: In this paper, the authors proposed EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task, such as applications, documents, contact information, and several keywords determining the task.
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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.
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![Figure 1: Two states of EntityBot’s user interface [11]. Recommended entities are displayed within four rows, here with five items each: people, applications, documents, and topics. The user can select entities of interest by clicking on them, which updates the recommendations. Example: In (a), the user sees entities related to her current work. She notices figures she has made for one of her papers (a1). She clicks on “Illustrator” (an application for editing vector graphics) (a2), then on the topic “diagram” (a3). (b) As a result, the entities of interest are displayed in the top area (b1) and the system updates the recommendations accordingly with the user’s selection. In the documents row, she selects an illustration (b2) that she will modify for use in her new paper.](/figures/figure-1-two-states-of-entitybots-user-interface-11-12n67oc2.webp)
Citations
Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender Systems
Mahta Bakhshizadeh
- 08 Oct 2024
TL;DR: This paper explores the application of context-aware recommender systems for personal information assistance to enhance productivity of knowledge workers, proposing a framework, collecting a pioneering dataset, and establishing a benchmark for evaluating recommendation scenarios.
1
Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data
Mahta Bakhshizadeh,Heiko Maus,Andreas Dengel +2 more
- 08 Oct 2024
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