Product recommendation recipe
The Product Recommendations recipe enables you to provide personalized product recommendations that are tailored to your customer’s needs and interests. With an accurate prediction model, a customer’s purchase history can provide you with insight on what products they may be interested in.
Who is this recipe built for?
In modern day, a retailer can offer a multitude of products, giving their customers a lot of choices which can also hinder their customers search. Due to time and effort constraints, customers may not find the product they desire, resulting in purchases with a high level of cognitive dissonance or no purchase at all.
What does this recipe do?
The Product Recommendations recipe uses machine learning to analyze a customer’s interactions with products in the past, and generate a personalized list of product recommendations quickly and effortlessly. This optimizes the product discovery process and eliminates long, unproductive, irrelevant searches for your customers. As a result, the Product Recommendations recipe can improve a customer’s overall purchasing experience, leading to higher engagement and stronger brand loyalty.
How do I get started?
You can get started by following the notebook to recipe workflow to create the Product Recommendations recipe in a Jupyter Notebook, and implement the recipe in Experience Platform Data Science Workspace.
Data schema
This recipe uses custom XDM schemas to model the input and output data:
Input data schema
Output data schema
Algorithm
The Product Recommendations recipe utilizes collaborative filtering to generate a personalized list of product recommendations for your customers. Collaborative filtering, unlike a content based approach, does not require information about a specific product but rather utilizes a customer’s historical preferences on a set of products. This powerful recommendation technique uses two simple assumptions:
- There are customers with similar interests, and they can be grouped by comparing their purchasing and browsing behaviors.
- A customer is more likely to be interested in a recommendation based off of similar customers in terms of their purchasing and browsing behavior.
This process is broken down into two main steps. First, define a subset of similar customers. Then, within that set, identify similar features among those customers in order to return a recommendation for the target customer.