老年人群菜品推荐AI训练数据

老年人群菜品推荐AI训练数据的价值在于其为构建精准、高效的老年人群菜品推荐AI模型提供了详尽且具针对性的信息基础。这些数据涵盖了老年人的关键特征，包括年龄、性别、健康状况和饮食习惯，使AI模型能够深入学习并掌握这些因素对菜品选择的影响。通过利用这些数据进行训练，AI模型能够更加准确地识别老年人的营养需求和饮食偏好，进而在实际应用中提供更加个性化的菜品推荐。这一训练过程的核心价值在于提升AI模型的预测精确度和适应能力，确保其在面对现实世界的复杂多变情况时，能够做出更加贴合老年人实际需求的决策。1.数据生成与预处理：使用Featuretools（一种特征生成工具）随机生成老年人群的特征信息，包括生成时间、人群类别、年龄、性别、健康状况、饮食习惯。通过数据清洗去除无效或错误记录，确保数据质量。
2.特征工程：跟据生成时间、年龄、性别、健康状况和饮食习惯，使用Feature-engine工具进行特征转换，生成特征标签。
3.菜品筛选：跟据特征标签，调用老年人群带量菜品知识库，运用SQL查询筛选出符合条件的推荐菜品。到此步骤为止，老年人群的特征信息、特征标签和推荐菜品共同构成训练集。
4.深度学习架构选择：采用深度交叉网络（DCN）作为深度学习架构。
5.模型训练：运用训练集对DCN模型进行训练。使用二元交叉熵损失函数来优化模型。采用Adam优化器进行参数更新。使用正则化技术（如L2正则化）来防止过拟合。
6.模型评估：使用准确率、召回率和F1分数来评估模型性能。通过交叉验证来评估模型的稳定性PSI。

The value of AI training data for senior population dish recommendation lies in providing a comprehensive and targeted information foundation for building accurate and efficient AI models for senior dish recommendation. This dataset covers key characteristics of seniors, including age, gender, health status and dietary habits, enabling AI models to deeply learn and grasp the impact of these factors on dish selection. By training with this data, AI models can more accurately identify seniors' nutritional needs and dietary preferences, thereby delivering more personalized dish recommendations in practical applications. The core value of this training process is to improve the prediction accuracy and adaptability of AI models, ensuring that they can make decisions that better align with the actual needs of seniors when facing complex and changing real-world scenarios.
1. Data Generation and Preprocessing: Use Featuretools (a feature generation tool) to randomly generate characteristic information of the senior population, including generation time, population category, age, gender, health status and dietary habits. Perform data cleaning to remove invalid or erroneous records and ensure data quality.
2. Feature Engineering: Based on the generation time, age, gender, health status and dietary habits, use the Feature-engine tool to perform feature transformation and generate feature labels.
3. Dish Screening: Based on the feature labels, call the portioned dish knowledge base for the senior population, and use SQL queries to filter eligible recommended dishes. Up to this step, the senior population's characteristic information, feature labels and recommended dishes jointly form the training dataset.
4. Deep Learning Architecture Selection: Adopt the Deep & Cross Network (DCN) as the deep learning architecture.
5. Model Training: Train the DCN model using the training dataset. Use the binary cross-entropy loss function to optimize the model. Adopt the Adam optimizer for parameter updates. Apply regularization techniques (such as L2 regularization) to prevent overfitting.
6. Model Evaluation: Use accuracy, recall and F1-score to evaluate model performance. Conduct cross-validation to evaluate the model's stability via PSI.

搜集汇总

数据集介绍

特点

该数据集是针对老年人群的菜品推荐AI训练数据，包含575条记录，涵盖了老年人的年龄、性别、健康状况和饮食习惯等关键特征，并提供了基于深度交叉网络（DCN）的菜品推荐模型训练和评估结果。