Information retrieval systems often consider search-session and immediately preceding web-browsing history as the context for predicting users’ present information needs. However, such context is only available when a user’s information needs originate from web context or when users have issued preceding queries in the search session. Here, we study the effect of more extensive context information recorded from users’ everyday digital activities by monitoring all information interacted with and communicated using personal computers. Twenty individuals were recruited for 14 days of 24/7 continuous monitoring of their digital activities, including screen contents, clicks, and operating system logs on Web and non-Web applications. Using this data, a transformer architecture is applied to model the digital activity context and predict representations of personalized information needs. Subsequently, the representations of information needs are used for query prediction, query auto-completion, selected search result prediction, and Web search re-ranking. The predictions of the models are evaluated against the ground truth data obtained from the activity recordings. The results reveal that the models accurately predict representations of information needs improving over the conventional search session and web-browsing contexts. The results indicate that the present practice for utilizing users’ contextual information is limited and can be significantly extended to achieve improved search interaction support and performance.

信息检索系统通常将搜索会话和之前的网络浏览历史视为预测用户当前信息需求的上下文。然而，只有当用户的信息需求源自网络上下文或者当用户在搜索会话中发出先前的查询时，这样的上下文才可用。在这里，我们通过监控与个人计算机交互和通信的所有信息来研究从用户日常数字活动中记录的更广泛的上下文信息的影响。招募了 20 个人，对他们的数字活动进行 14 天 24/7 的连续监控，包括 Web 和非 Web 应用程序上的屏幕内容、点击和操作系统日志。使用这些数据，应用变压器架构来对数字活动上下文进行建模并预测个性化信息需求的表示。随后，信息需求的表示用于查询预测、查询自动完成、选定的搜索结果预测和网络搜索重新排序。模型的预测是根据从活动记录中获得的真实数据进行评估的。结果表明，与传统搜索会话和网络浏览环境相比，该模型能够准确预测信息的表示需要改进。结果表明，目前利用用户上下文信息的实践是有限的，并且可以显着扩展以实现改进的搜索交互支持和性能。