Purpose – In the context of increased digitization in companies, technologies are required that support personalized training in informal learning environments. Meanwhile, knowledge recommender systems suggested in academia do not sufficiently consider the dynamic and task-oriented preferences of employees as well as the demands of enterprises. This paper aims to fill this research gap by proposing a novel, innovative architecture of a knowledge-based recommender system and by providing practitioners with guidance in selecting an appropriate training course that meets the employee's needs while also taking into account corporate demands. Research Strategy – To develop the knowledge-based recommender system, this paper follows a Design Science Research approach. It does so by examining an international case company based in Switzerland with the goal of developing a generalized solution for the global market. Expert interviews and participant questionnaires were deployed to collect data and to understand the current state of corporate training in the case company and employee interviews were held to evaluate the validity of the recommender system. Findings – The paper concludes that a knowledge-based system can recommend a personalized lesson plan with the appropriate learning method based on company demands, the employee's work context and his or her learning type. The study specifies all the parameters that must be integrated into an according system and shows that the personalized learning approach, which has been proven in schools and universities, can also be applied in the corporate environment.