Product Recommendation System

Product Recommendation System for e-commerce businesses

The recommendation system is designed in 3 parts based on the business context:

• Recommendation system part I: Product pupularity based system targetted at new customers

• Recommendation system part II: Model-based collaborative filtering system based on customer's purchase history and ratings provided by other users who bought items similar items

• Recommendation system part III: When a business is setting up its e-commerce website for the first time withou any product rating

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Small piece from code

correlation_product_ID = correlation_matrix[product_ID]

i = '6117043058'

product_names = list(X.index)
product_ID = product_names.index(i)
     
Recommend = list(X.index[correlation_product_ID > 0.90])
Recommend.remove(i)
Recommend[0:9]

Output: 

['0205616461',
 '0558925278',
 '0737104473',
 '1304146537',
 '1304168522',
 '1304196070',
 '1304351475',
 '1304482596',
 '1304482634']

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Datasets

• Amazon product review dataset
• Dataset: Home Depot's dataset with product dataset