Department of Computer Science and Engineering (DISI), Alma Mater Studiorum - Università di Bologna, via dell'Università 50, Cesena (FC), 47522, Italy.
Department of Computer Science, Özyeğin University, Nisantepe Mah. Orman Sok. No:34-36 Alemdağ, Çekmeköy, Istanbul 34794, Türkiye.
Comput Methods Programs Biomed. 2023 Jun;235:107536. doi: 10.1016/j.cmpb.2023.107536. Epub 2023 Apr 5.
This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts' prescriptions, (ii) adherence to users' tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users-hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts' prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users' preferences from data, convert them into symbolic form, and extend them with experts' goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.
本文专注于营养推荐系统 (RS)，即通过人工智能自动为用户提供有关饮食建议，以帮助他们实现体重/体型目标。在设计此类系统时，必须考虑到（潜在）冲突需求之间的权衡，包括：（i）遵守专家的处方，（ii）遵守用户的口味和偏好，（iii）整个推荐过程的可解释性。因此，在本文中，我们提出了一种新的营养 RS 工程方法，将机器学习和符号知识提取相结合，以对用户进行分析，从而协调上述需求。
方法：我们的贡献主要集中在数据处理工作流程上。从针对用户偏好进行预测的神经网络 (NN) 训练开始，我们使用 CART （Breiman 等人，1984 年）在 Prolog （Körner 等人，2022 年）形式中提取符号规则，并将其与以类似形式提供的专家处方结合起来。然后，我们可以通过逻辑求解器查询由此产生的符号知识库，以得出可解释的推荐。
结果：我们在一个包含 45723 个食谱的公开数据集上进行了实验，此外还有 12 个关于相同数量想象用户的合成数据集，以及 6 个专家处方。我们在这些数据集上训练全连接 4 层 NN，平均达到约 86%的测试集准确率。反过来，提取的规则与那些 NN 的相似度约为 80%。由此产生的推荐系统的测试集精度约为 74%。符号方法使得智能代理可以从数据中学习用户的偏好，将其转换为符号形式，并将其与专家的目标导向处方扩展。由此产生的推荐对于最终用户来说是可接受的，并且从营养角度来看是足够的，同时推荐生成过程是可解释的。
