Hello Hackers! I would like to share this notebook with you, which I wrote in order to explain how recommendation algorithms work, in an easy to understand way for beginners.
I will go deeper into building a product recommendation system that we can better target customers with, using product recommendations that are tailored to individual customers. Studies have shown that customized product recommendations improve conversion rates and customer retention rates.
A product recommendation system is a system whose objective is to predict and compile a list of items that a customer is likely to buy. Referral systems have gained much popularity in recent years and have been developed and implemented for various commercial use cases
For example,
The media service provider, Netflix, uses referral systems to recommend movies or television programs for individual users who are likely to watch
The e-commerce company Amazon uses recommendation systems to predict and display a list of products that the customer is likely to buy
The music streaming service, Pandora, uses music recommendation systems for its listeners.
The use of a referral system does not stop here. It can also be used to recommend users related articles, news or books
With the potential to be used in a variety of areas, referral systems play a critical role in many businesses, especially e-commerce and media businesses, as they directly impact sales revenue and user engagement
You can download the code and the whole explanation here: https://www.narrativetext.co/the-analyst/building-a-product-recommendation-system-with-collaborative-filtering
