AI在个性化学习内容生成中的应用：适应学习风格

    def recommend_content(learning_style, content_library):
    """
    根据学习风格推荐个性化学习内容
    
    :param learning_style: 学习风格
    :param content_library: 学习内容库
    :return: 个性化学习内容列表
    """
    recommended_content = []
    for content in content_library:
        if content['style'] == learning_style:
            recommended_content.append(content)
    return recommended_content
    
    # 示例
    content_library = [
    {'title': 'Python入门', 'style': '理论'},
    {'title': '深度学习实战', 'style': '实践'},
    {'title': '算法面试题', 'style': '理论'},
    {'title': '前端开发教程', 'style': '实践'}
    ]
    
    learning_style = '实践'
    print(recommend_content(learning_style, content_library))

    import numpy as np
    
    def collaborative_filter_recommend(user_preferences, content_preferences, k=3):
    """
    基于协同过滤推荐个性化学习内容
    
    :param user_preferences: 用户偏好矩阵
    :param content_preferences: 学习内容偏好矩阵
    :param k: 邻居个数
    :return: 个性化学习内容列表
    """
    similar_contents = []
    for content in content_preferences:
        similarity = np.dot(user_preferences, content_preferences[content])
        similar_contents.append((content, similarity))
    similar_contents.sort(key=lambda x: x[1], reverse=True)
    neighbors = similar_contents[:k]
    recommended_content = []
    for neighbor in neighbors:
        for content in content_preferences[neighbor[0]]:
            if content not in recommended_content:
                recommended_content.append(content)
    return recommended_content
    
    # 示例
    user_preferences = np.array([0.5, 0.5])
    content_preferences = {
    'Python入门': np.array([0.8, 0.2]),
    '深度学习实战': np.array([0.2, 0.8]),
    '算法面试题': np.array([0.5, 0.5]),
    '前端开发教程': np.array([0.5, 0.5])
    }
    print(collaborative_filter_recommend(user_preferences, content_preferences))