智能编辑新购活动

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

# 假设我们有一个包含商品信息和用户偏好的数据集
data = pd.read_csv('products.csv')

# 使用TF-IDF向量化商品描述
tfidf = TfidfVectorizer(stop_words='english')
data['description'] = data['description'].fillna('')
tfidf_matrix = tfidf.fit_transform(data['description'])

# 计算商品之间的相似度
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

# 定义一个函数来获取推荐商品
def get_recommendations(title, cosine_sim=cosine_sim):
    idx = data.index[data['title'] == title].tolist()[0]
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:11]  # 获取前10个最相似的商品
    product_indices = [i[0] for i in sim_scores]
    return data['title'].iloc[product_indices]

# 示例调用
print(get_recommendations('Sample Product Title'))