WorkR：面向智能任务辅助的职业推断
WorkR: Occupation Inference for Intelligent Task Assistance
AI总结：
本文提出 WorkR 框架，利用被动感知和变分自编码器提取潜在特征以推断用户职业，无需持续手动输入，在六个 ISO 职业类别中实现了超过 91% 的推断准确率，为数字助手提供个性化任务支持。
摘要：
职业信息可被数字助手用于提供针对特定职业的个性化任务支持，包括中断管理、任务规划和推荐。数字工作场所助手领域的先前研究要求用户输入其职业信息以实现有效支持。然而，由于许多个体每天在多个职业间切换，若无持续的用户输入，当前解决方案便会失效。为解决此问题，本研究引入了 WorkR 框架，该框架利用被动感知从各类任务活动中捕获泛在信号，应对三大挑战：缺乏被动感知架构、职业特征个性化，以及发现职业变量间的潜在关系。我们认为，来自应用使用、移动、社交互动和环境的信号能够反映用户的职业。WorkR 使用变分自编码器（VAE）提取潜在特征，以训练模型推断职业。我们使用一个匿名的、富含上下文的活动与任务日志数据集进行实验，结果表明我们的模型能在六个 ISO 职业类别中以超过 91% 的准确率准确推断职业。
英文摘要：
Occupation information can be utilized by digital assistants to provide occupation-specific personalized task support, including interruption management, task planning, and recommendations. Prior research in the digital workplace assistant domain requires users to input their occupation information for effective support. However, as many individuals switch between multiple occupations daily, current solutions falter without continuous user input. To address this, this study introduces WorkR, a framework that leverages passive sensing to capture pervasive signals from various task activities, addressing three challenges: the lack of a passive sensing architecture, personalization of occupation characteristics, and discovering latent relationships among occupation variables. We argue that signals from application usage, movements, social interactions, and the environment can inform a user's occupation. WorkR uses a Variational Autoencoder (VAE) to derive latent features for training models to infer occupations. Our experiments with an anonymized, context-rich activity and task log dataset demonstrate that our models can accurately infer occupations with more than 91% accuracy across six ISO occupation categories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
