标题： MOPI-HFRS：一种多目标个性化健康感知食品推荐系统，具有大模型增强的解释性 显示英文标题

标题： MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation

摘要： 不健康饮食习惯的普遍性在美国已成为日益令人关注的问题。 然而，主要的食品推荐平台（例如，Yelp）仍然优先考虑用户的饮食偏好，而不是他们选择的健康程度。 尽管已经做出了努力来开发健康意识的食品推荐系统，但基于用户特定健康状况的个性化研究仍缺乏深入探讨。 此外，很少有研究关注这些系统的可解释性，这阻碍了用户评估推荐的可靠性，并妨碍了这些系统的实际部署。 为了解决这一差距，我们首次建立了两个大规模个性化健康意识食品推荐基准。 然后，我们开发了一个新的框架， 多目标个性化可解释健康意识食品推荐系统（MOPI-HFRS），通过联合优化三个目标：用户偏好、个性化健康性和营养多样性，以及一个由大型语言模型（LLM）增强的推理模块，通过解释推荐结果来促进健康的饮食知识。 具体来说，这个整体图学习框架首先利用两个结构学习和一个结构池化模块来利用描述性特征和健康数据。 然后，它采用帕累托优化来实现设计的多方面目标。 最后，为了进一步促进健康的饮食知识和意识，我们通过知识注入利用一个LLM，用从推荐模型中获得的知识提示LLM进行解释。 显示英文摘要

摘要： The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platforms (e.g., Yelp) continue to prioritize users' dietary preferences over the healthiness of their choices. Although efforts have been made to develop health-aware food recommendation systems, the personalization of such systems based on users' specific health conditions remains under-explored. In addition, few research focus on the interpretability of these systems, which hinders users from assessing the reliability of recommendations and impedes the practical deployment of these systems. In response to this gap, we first establish two large-scale personalized health-aware food recommendation benchmarks at the first attempt. We then develop a novel framework, Multi-Objective Personalized Interpretable Health-aware Food Recommendation System (MOPI-HFRS), which provides food recommendations by jointly optimizing the three objectives: user preference, personalized healthiness and nutritional diversity, along with an large language model (LLM)-enhanced reasoning module to promote healthy dietary knowledge through the interpretation of recommended results. Specifically, this holistic graph learning framework first utilizes two structure learning and a structure pooling modules to leverage both descriptive features and health data. Then it employs Pareto optimization to achieve designed multi-facet objectives. Finally, to further promote the healthy dietary knowledge and awareness, we exploit an LLM by utilizing knowledge-infusion, prompting the LLMs with knowledge obtained from the recommendation model for interpretation.