【腾讯云产品最佳实践】新一代“最强CDN”——EdgeOne 性能优势之深度解析

# 假设我们有一个简单的产品推荐系统，它根据用户的浏览历史来推荐产品

def recommend_products(user_history, product_catalog):
    # 基于用户的浏览历史推荐产品
    recommended = []
    for item in user_history:
        if item in product_catalog:
            recommended.append(product_catalog[item])
    return recommended

# 假设EdgeOne平台已经缓存了产品目录和用户的浏览历史
product_catalog = {"laptop": "High-performance laptop", "smartphone": "Latest smartphone model"}
user_history = ["smartphone", "laptop"]

# 在EdgeOne平台上调用推荐系统
recommended_items = recommend_products(user_history, product_catalog)
print("Recommended items:", recommended_items)

def dynamic_pricing(product_id, inventory, user_location, user_history):
    # 根据库存、用户位置和购买历史动态调整价格
    if inventory[product_id] < 10:
        price_multiplier = 1.2
    else:
        price_multiplier = 1.0
    
    if user_location in ["New York", "Los Angeles"]:
        price_multiplier *= 1.1
    
    for item in user_history:
        if item == product_id:
            price_multiplier *= 0.9  # 给予回头客折扣
    
    base_price = inventory[product_id]['price']
    return base_price * price_multiplier

# 假设EdgeOne平台已经缓存了库存信息和用户信息
inventory = {
    "laptop": {"price": 1000, "stock": 5},
    "smartphone": {"price": 500, "stock": 20}
}
user_location = "New York"
user_history = ["smartphone"]

# 在EdgeOne平台上调用动态定价系统
new_price = dynamic_pricing("laptop", inventory, user_location, user_history)
print(f"The dynamic price for the laptop is: ${new_price}")

def analyze_user_behavior(user_actions):
    # 分析用户行为，提供优化建议
    behavior_summary = {}
    for action in user_actions:
        if action['type'] in behavior_summary:
            behavior_summary[action['type']] += 1
        else:
            behavior_summary[action['type']] = 1
    
    return behavior_summary

# 假设EdgeOne平台已经缓存了用户行为数据
user_actions = [
    {"type": "view", "item": "laptop"},
    {"type": "add_to_cart", "item": "smartphone"},
    {"type": "view", "item": "smartphone"},
    {"type": "purchase", "item": "smartphone"}
]

# 在EdgeOne平台上调用用户行为分析系统
behavior_summary = analyze_user_behavior(user_actions)
print("User behavior summary:", behavior_summary)

def cache_product_details(product_id, edge_nodes):
    # 将商品详情缓存到边缘节点
    for node in edge_nodes:
        node.cache_product(product_id)
    
    print(f"Product {product_id} details cached in edge nodes.")

# 假设EdgeOne平台已经定义了边缘节点
edge_nodes = ["Node1", "Node2", "Node3"]

# 在EdgeOne平台上调用边缘缓存系统
cache_product_details("smartphone", edge_nodes)