Foods, Vol. 12, Pages 2079: Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning
Current food recommender systems tend to prioritize either the user&rsquo;s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user&rsquo;s personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user&ndash;recipe interactions, recipe&ndash;ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user&rsquo;s requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users&rsquo; dietary preferences and personalized health requirements in food recommendations and performed best on the health task.
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