Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data.

中文翻译：

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日常数字任务的实体推荐

推荐系统可以通过在上下文中检索和推荐有用的信息来支持日常数字任务。这在服务和操作系统中变得越来越重要。以前的研究通常侧重于特定的推荐任务，这些任务是从与单个应用程序的交互中捕获的数据。推荐的质量也经常仅针对准确性的计算度量进行评估，而没有调查推荐在实际任务中的有用性。这项工作的目的是通过（1）展示全面的数字活动监控，（2）引入基于实体的计算和交互，以及（3）调查之前被忽视的实体推荐的有用性，通过一种新颖的方法综合该领域的研究以及它们在实际任务中对用户行为的实际影响。该方法利用每 2 秒记录的屏幕帧中的上下文来推荐与当前任务相关的信息实体。我们将这种方法体现在一个交互式系统中，并在一项研究中调查了推荐实体的相关性和影响，参与者在 14 天的监测阶段后恢复了他们的实际任务。结果表明，与没有系统的控制相比，这些建议允许参与者找到更多相关实体。此外，在实际任务中也使用了推荐的实体。在讨论中，我们在上下文中反思实体推荐的研究议程，重新审视全面监控以包括物理世界，将实体视为可操作的建议，捕捉漂移的意图和例程，