双12企业画像推荐

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

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

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

# 推荐函数
def recommend(user_id, ratings, user_similarity):
    user_ratings = ratings[user_id]
    similar_users = user_similarity[user_id].argsort()[::-1][1:]
    
    recommendations = np.zeros(ratings.shape[1])
    for similar_user in similar_users:
        if np.sum(user_ratings == 0) == 0:  # 如果当前用户已评分所有商品，则停止推荐
            break
        recommendations += user_similarity[user_id][similar_user] * (ratings[similar_user] - user_ratings)
    
    return recommendations

# 为用户0推荐商品
recommendations = recommend(0, ratings, user_similarity)
print("推荐分数:", recommendations)