商圈小程序推荐商品

# 示例：基于物品的协同过滤推荐算法
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

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

# 计算商品间的相似度
item_similarity = cosine_similarity(ratings.T)

# 推荐函数
def recommend_items(user_id, ratings, item_similarity, top_k=2):
    user_ratings = ratings[user_id]
    similar_items = item_similarity.dot(user_ratings)
    # 排除已评分的商品
    similar_items[user_ratings.nonzero()] = -np.inf
    # 获取推荐商品的索引
    recommended_indices = np.argsort(similar_items)[-top_k:]
    return recommended_indices

# 为用户0推荐商品
recommended_items = recommend_items(0, ratings, item_similarity)
print("推荐给用户的商品索引:", recommended_items)