From the course: Predictive Customer Analytics
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User-item affinity and recommendations - Python Tutorial
From the course: Predictive Customer Analytics
User-item affinity and recommendations
- [Instructor] Generally, for business purposes, customers are grouped based on various attributes. For example, Roger might be grouped by a bank as a middle-aged executive based on his age or as a gold customer based on his high account balance. Jessica might be grouped as a young student based on her occupation and age. In the same way, products are also grouped based on their type or use. Like a laptop might be grouped as electronics based on the type or a backpack might be grouped as college supplies based on its use. Businesses try to build recommendation engines based on these groups. A new age of recommendation engines have become very popular today and they are called collaborative filtering recommendation engines. Collaborative filtering works on just three pieces of data: a user or a customer, an item and an affinity score between the user and the item. For example, in Amazon, the user is the buyer, the…
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