12.12社交营销推荐

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

# 示例数据：产品ID、产品名称和产品描述
data = {
    'product_id': [1, 2, 3, 4],
    'name': ['Product A', 'Product B', 'Product C', 'Product D'],
    'description': ['This is Product A', 'This is Product B', 'This is Product C', 'This is Product D']
}
df = pd.DataFrame(data)

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

# 计算产品之间的相似度
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

# 推荐函数
def get_recommendations(product_id, cosine_sim=cosine_sim):
    idx = df[df['product_id'] == product_id].index[0]
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:3]  # 获取最相似的两个产品（除了自身）
    product_indices = [i[0] for i in sim_scores]
    return df['name'].iloc[product_indices]

# 示例调用
recommended_products = get_recommendations(1)
print(recommended_products)  # 输出推荐的产品名称