Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks. These RS anticipate user needs by leveraging personal knowledge modeling, context recognition, and recommendation techniques to deliver timely and relevant resources without requiring explicit searches. Developing such RS faces various challenges in addition to common challenges within the broader RS landscape. These challenges range from data‐related issues, such as handling heterogeneous and noisy data, to user‐centric concerns, including privacy, explainability, and the lack of explicit feedback, as well as system and algorithmic challenges like cross‐application context modeling and scalability. This paper explores the opportunities, challenges, and future directions for PID, outlining key advancements and enabling technologies that support its development.
Bakhshizadeh et al. (Thu,) studied this question.