Personalized and Adaptive Diet Recommendation for Individuals with Cardiovascular Diseases

Given the profound impact of diet on cardiovascular health, appropriate dietary intake in home cooking is widely acknowledged as being instrumental in preventing cardiovascular diseases (CVDs). However, contemporary dietary recommendation systems for patients with CVDs are limited by population-level guidelines, static advice, as well as a lack of consideration for contextual conditions and individual preferences. We hence subscribe to the Theory of Planned Behavior (TPB) to design a novel recipe recommender system. Our proposed recipe recommender system takes into account users’ personal attitudes, subjective norms, and perceived behavioural control as focal considerations in recipe recommendations. Particularly, our recipe recommendation system assimilates personalized considerations—including health conditions, taste preferences, and available ingredients in the user's refrigerator—with social elements in the likes of cooking frequency, recipe viewing intensity, and user comments to bolster the acceptability of recommended recipes. Additionally, by incorporating user interactions in the likes of adding and liking recipes, our proposed recipe recommender system streamlines recipe discovery and strengthens users’ perceived behavioural control to maintain dietary choices. This integration aims to alleviate the practical challenges associated with adopting recommended recipes, rendering it much easier for users to adhere to dietary guidelines. Based on empirical validation, we not only deliver detailed insights into the implementation of a recipe recommendation system, but we also validate its utility in enhancing user engagement and satisfaction.