input: Write a function to create a recommendation system in Python using Pandas that recommends new products to customers based on past purchases. The function should read product data from a CSV file, create an item-based collaborative filter using cosine similarity, and generate recommendations for each product.
  assert create_recommendation_system('product_data.csv')
  # Assume the CSV file contains relevant product data and the output is printed as recommended items for each product.
output: import pandas as pd
  from sklearn.metrics.pairwise import cosine_similarity
  
  def create_recommendation_system(file_path):
      df = pd.read_csv(file_path)
      pivot_table = df.pivot_table(index='customer_id', columns='product_id', values='quantity', fill_value=0)
      item_similarity = cosine_similarity(pivot_table.T)
      item_similarity_df = pd.DataFrame(item_similarity, index=pivot_table.columns, columns=pivot_table.columns)
      
      for product in item_similarity_df.columns:
          similar_products = item_similarity_df[product].sort_values(ascending=False).index[1:4]
          print(f"Recommended for product {product}: {', '.join(similar_products)}")