Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people’s food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me , a personalized nutrient-based meal recommender system designed to meet individuals’ nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist . We demonstrate FoodDist’s superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

中文翻译：

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百胜我

基于营养的膳食建议有可能帮助个人预防或管理糖尿病和肥胖等疾病。然而，了解人们的食物偏好并提出既能吸引他们的口味又能满足营养期望的建议是具有挑战性的。现有的方法要么只学习高级别的偏好，要么需要较长的学习期。我们建议好吃我，一个个性化的基于营养的膳食推荐系统，旨在满足个人的营养期望、饮食限制和细粒度食物偏好。Yum-me 通过基于视觉测验的用户界面实现了一个简单而准确的食物偏好分析程序，并将学习的概况投射到营养适当的食物选择领域，以找到对用户有吸引力的食物。我们介绍了 Yum-me 的设计和实现，并进一步描述和评估了两项创新贡献。第一个贡献是一个开源的最先进的食物图像分析模型，名为食品区. 我们通过仔细的基准测试展示了 FoodDist 的卓越性能，并讨论了它在各种饮食应用中的适用性。第二个贡献是一个新颖的在线学习框架，它从逐项和成对的图像比较中学习食物偏好。我们在对 227 名匿名用户的实地研究中评估了该框架，并证明它大大优于其他基线。我们进一步通过 60 人的用户研究对 Yum-me 的可行性和有效性进行了端到端验证，其中 Yum-me 将推荐接受率提高了 42.63%。