**AI-Powered Personalized Product Recommendation**

AI-Powered Personalized Product Recommendation

Here's a compact code snippet using a novel approach to AI in Advertising:

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

# Load product and customer data
products = pd.read_csv('products.csv')
customers = pd.read_csv('customers.csv')

# Create TF-IDF vectorizer for product descriptions
vectorizer = TfidfVectorizer(stop_words='english')

# Transform product descriptions and customer searches
product_vectors = vectorizer.fit_transform(products['description'])
customer_vectors = vectorizer.transform(customers['search_history'])

# Calculate cosine similarity between customer searches and product descriptions
from sklearn.metrics.pairwise import cosine_similarity
similarities = cosine_similarity(customer_vectors, product_vectors)

# Recommend top 3 products based on similarity score
recommended_products = products.loc[similarities.argsort()[:, ::-1][:, :3]]

print(recommended_products)

This code snippet uses a TF-IDF (Term Frequency-Inverse Document Frequency) vectorizer to transform customer search history and product descriptions into numerical vectors. It then calculates the cosine similarity between these vectors to recommend products that best match the customer's search interests.

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