双十一应用对接云推荐

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# 用户-物品评分矩阵
ratings = np.array([
    [4, 0, 0, 5],
    [0, 5, 3, 0],
    [1, 0, 0, 4],
    [0, 2, 4, 0]
])

# 计算用户相似度
user_similarity = cosine_similarity(ratings)

# 推荐函数
def recommend(user_id):
    similar_users = user_similarity[user_id].argsort()[::-1][1:]
    recommended_items = set()
    
    for similar_user in similar_users:
        items_rated_by_similar_user = np.where(ratings[similar_user] > 0)[0]
        recommended_items.update(items_rated_by_similar_user)
        
        if len(recommended_items) >= 3:  # 推荐3个物品
            break
            
    return list(recommended_items)

# 示例调用
print(recommend(0))  # 为用户0推荐物品