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.

翻译：推荐算法发展的一个新趋势是将其能力扩展到支持人群进行健康管理。本文聚焦于提升心血管疾病（CVD）预防有效性的问题，因为CVD是全球首要死因。为此，提出了一种基于知识的推荐算法，以支持成年人在家中对CVD风险因素进行自我管理。该算法基于原创的多维推荐模型和新的用户画像模型，后者除包含官方指南所述的当前CVD健康评估外，还纳入了预测性评估。该算法的主要特点是将基于规则的逻辑与大型语言模型生成类人文本的能力相结合，用于多维推荐的解释性组件。对所提出算法的验证与评估表明，该推荐算法对于支持成年人在家中自我管理CVD风险因素具有实用性。与同类基于知识的推荐算法相比，该算法能够评估更多CVD风险因素，且生成的推荐信息具有更强的信息容量和语义容量。