期刊
15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021)
卷 -, 期 -, 页码 753-756出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3460231.3478883
关键词
Proactive information retrieval; real-world tasks; user intent modeling
资金
- EU H2020 project CO-ADAPT
- MIUR (PON AIM)
- Academy of Finland [322653, 328875, 336085, 319264, 292334]
- Academy of Finland (AKA) [322653, 328875, 292334, 319264, 328875, 322653, 336085, 319264, 292334] Funding Source: Academy of Finland (AKA)
The system, EntityBot, captures user context across application boundaries to recommend relevant information entities for current tasks. By continuously monitoring digital activity, it effectively detects task context and retrieves entities, leading to high user satisfaction in real-world tasks.
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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