双十一图像分析推荐

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

# 示例商品数据
products = {
    'product1': '时尚新款连衣裙',
    'product2': '高跟鞋',
    'product3': '运动鞋',
    'product4': '牛仔裤'
}

# 用户历史购买记录
user_history = ['product1', 'product2']

# 将商品描述转换为TF-IDF向量
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(products.values())

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

# 推荐函数
def get_recommendations(title, cosine_sim=cosine_sim):
    idx = list(products.keys()).index(title)
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:3]  # 取前两个最相似的商品
    product_indices = [i[0] for i in sim_scores]
    return list(products.keys())[product_indices]

# 获取推荐商品
recommended_products = get_recommendations('product1')
print("推荐商品:", recommended_products)