From the course: Predictive Customer Analytics
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Use case: Recommend items - Python Tutorial
From the course: Predictive Customer Analytics
Use case: Recommend items
- [Instructor] In this lecture, we are going to implement the use case for building recommendations. When a customer comes to your website and has bought a product, you want to recommend additional products that similar customers bought. So, in this exercise, we are going to use data about which customers bought which product, and based on that, build an item-to-item affinity score and then use it for recommendation. The data file for this exercise is Data-03-05-user-item-rating-data.csv. And the notebook is NB-03-05-recommending-items-to users. Let's explore the data file. We have userIds and ItemIds. User 1001 bought 5001, 5002, and 5005. Pretty simple and straightforward data. Moving on to the notebook, let's now implement the recommendation algorithm. First, we are going to load the rating csv file and take a look at it. Now, we want to build an affinity score between the items based on the users who bought them. There are a number of out of the box, open source, as well as…
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