Recommender systems are extensively employed across various domains to mitigate information overload by providing personalized content. Despite their widespread use in sectors such as streaming, social networks, and e-commerce, utilizing them for personal information assistance is a comparatively novel application. This emerging application aims to develop intelligent systems capable of proactively providing knowledge workers with the most relevant information based on their context to enhance productivity. In this paper, we explore this innovative application by first defining the scope of our study, outlining the key objectives, and introducing main challenges. We then present our current results and progress, including a comprehensive literature review, the proposal of a framework, the collection of a pioneering dataset, and the establishment of a benchmark for evaluating a recommendation scenario on our published dataset. We also discuss our ongoing efforts and future research directions.