  One of the new trends in the development of recommendation algorithms is the
dissemination of their capabilities to support the population in managing their
health. This article focuses on the problem of improving the effectiveness of
cardiovascular diseases (CVD) prevention, since CVD is the leading cause of
death worldwide. To address this issue, a knowledge-based recommendation
algorithm was proposed to support self-management of CVD risk factors in adults
at home. The proposed algorithm is based on the original multidimensional
recommendation model and on a new user profile model, which includes predictive
assessments of CVD health in addition to its current ones as outlined in
official guidelines. The main feature of the proposed algorithm is the
combination of rule-based logic with the capabilities of a large language model
in generating human-like text for explanatory component of multidimensional
recommendation. The verification and evaluation of the proposed algorithm
showed the usefulness of the proposed recommendation algorithm for supporting
adults in self-management of their CVD risk factors at home. As follows from
the comparison with similar knowledge-based recommendation algorithms, the
proposed algorithm evaluates a larger number of CVD risk factors and has a
greater information and semantic capacity of the generated recommendations.
