减肥人群菜品AI推荐数据

减肥人群菜品AI推荐数据的应用场景主要体现在为减肥者、减肥指导人员以及餐饮服务商提供智能化、精准化的菜品推荐服务。通过分析减肥人群的基本信息和饮食习惯，AI模型能够为每位减肥者推荐符合其低热量、高营养需求的菜品，有助于丰富减肥者的就餐选择。对于减肥指导人员而言，通过本数据可以更好地了解减肥者的饮食习惯和营养摄入情况，从而提供更科学的饮食建议。餐饮服务商也能通过这些数据来调整菜单，确保菜品的多样性和营养均衡，满足不同减肥者的需求。1.数据收集和预处理：（1）从公司订单系统抽取用户ID、抽取时间、人群类别、年龄、性别、健康状况、饮食习惯。（2）通过数据清洗去除无效或错误记录，确保数据质量。
2.特征生成：根据人群类别、年龄、性别、健康状况、饮食习惯，使用Feature-engine工具进行特征转换，生成特征标签。
3.实时预测：运用经公司自行训练和部署的基于深度交叉网络（DCN）深度学习架构的减肥人群菜品智能推荐模型，根据生成的特征标签，对菜品进行实时预测和推荐。
4.结果解释：利用SHAP方法对推荐菜品进行解释，确保结构对用户的可理解性和可解释性。
5.评价优化：收集用户对推荐菜品的反馈，利用反馈数据对模型进行进一步的迭代和优化。

The application scenarios of the AI dish recommendation dataset for weight-loss populations mainly focus on providing intelligent and precise dish recommendation services for dieters, weight-loss instructors, and catering service providers. By analyzing the basic information and dietary habits of weight-loss groups, the AI model can recommend dishes that meet the low-calorie and high-nutrition needs of each dieter, which helps enrich their dining options. For weight-loss instructors, this dataset can help them better understand the dietary habits and nutrient intake of dieters, thereby providing more scientific dietary advice. Catering service providers can also adjust their menus based on this data to ensure dish diversity and nutritional balance, meeting the needs of different dieters.

1. Data Collection and Preprocessing:
(1) Extract user ID, extraction timestamp, population category, age, gender, health status, and dietary habits from the company's order system.
(2) Remove invalid or erroneous records through data cleaning to ensure data quality.

2. Feature Generation:
Generate feature labels by performing feature transformation using the Feature-engine tool based on population category, age, gender, health status, and dietary habits.

3. Real-time Prediction:
Use the intelligent dish recommendation model for weight-loss populations that is self-trained and deployed by the company, based on the Deep & Cross Network (DCN) deep learning architecture. The model conducts real-time dish prediction and recommendation based on the generated feature labels.

4. Result Interpretation:
Utilize the SHAP method to interpret the recommended dishes, ensuring the understandability and interpretability of the recommendation results for users.

5. Evaluation and Optimization:
Collect user feedback on the recommended dishes, and use the feedback data to further iterate and optimize the model.

搜集汇总

数据集介绍

特点

该数据集专注于为减肥人群提供个性化的菜品推荐，包含用户的基本信息和饮食习惯，通过深度学习模型进行实时预测和推荐，旨在帮助减肥者选择低热量、高营养的菜品，同时为减肥指导人员和餐饮服务商提供科学依据。