12.12APP搜索 推荐

def content_based_recommendation(user_profile, item_profiles):
    recommendations = []
    for item_id, item_profile in item_profiles.items():
        similarity = calculate_similarity(user_profile, item_profile)
        recommendations.append((item_id, similarity))
    recommendations.sort(key=lambda x: x[1], reverse=True)
    return recommendations[:10]  # 返回前10个推荐项

def calculate_similarity(profile_a, profile_b):
    # 简单的余弦相似度计算示例
    dot_product = sum(a * b for a, b in zip(profile_a, profile_b))
    magnitude_a = sum(a ** 2 for a in profile_a) ** 0.5
    magnitude_b = sum(b ** 2 for b in profile_b) ** 0.5
    return dot_product / (magnitude_a * magnitude_b)

# 示例用法
user_profile = [0.8, 0.2, 0.1]  # 用户兴趣向量
item_profiles = {
    'item1': [0.7, 0.3, 0.2],
    'item2': [0.1, 0.8, 0.1],
    # ... 其他商品向量
}
recommendations = content_based_recommendation(user_profile, item_profiles)
print(recommendations)