the framework of dynamically reconmend the content of educational correction courses

SenCube

于 2024-05-11 10:29:37 发布

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分类专栏：深度学习文章标签：机器学习人工智能

本文链接：https://blog.youkuaiyun.com/LHS__BRU_745/article/details/138704496

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该系统结合多个因素设计动态推荐教育矫正课程，包括数据收集预处理、权重确定、个性化推荐算法及系统实施与反馈回路。通过专家评分、机器学习确定因素权重，根据矫正对象信息构建用户画像，计算课程推荐得分，实现动态调整和效果优化。

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Designing a system to dynamically develop the content of educational correction courses for community correction subjects requires a combination of factors to ensure personalization and targeting. The following is a conceptual scheme and algorithmic implementation framework.

1. Data collection and preprocessing

Data collection: First, the system needs to collect specific information about each correction object, including but not limited to criminal facts, community correction types, psychological evaluation results before and after correction, gender, age, employment and school records, family social relations, reward and punishment records, etc.

Data preprocessing: cleaning data, handling missing values, standardizing scores (e.g. psychological assessment results), and performing necessary coding (e.g. gender, age classification).

2. Factor weight determination

Expert scoring or

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