AI应用部署双十二活动

# 智能推荐系统简单示例代码
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

# 加载商品数据和用户行为数据
products = pd.read_csv('products.csv')
user_history = pd.read_csv('user_history.csv')

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

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

# 推荐函数
def get_recommendations(title, cosine_sim=cosine_sim):
    idx = products.index[products['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]  # 取前10个最相似的商品
    product_indices = [i[0] for i in sim_scores]
    return products['title'].iloc[product_indices]

# 示例调用
recommended_products = get_recommendations('某热门商品')
print(recommended_products)