February 1, 2024

How to Implement a Product Recommendation System with Snowflake

By Ben Leder
A badge that reads: Making the complex, easy

Have you ever wondered, 

“How did my phone know I wanted to buy this?!”.

It’s almost as if your devices know you better than you do yourself. How can this be possible? This is no magic trick. It is a feat of Machine Learning known as the Product Recommendation System.

In this post, we will be discussing these systems and how they operate so that your organization can begin its journey of delivering new market value. We will also be diving into the actual methods for implementing recommendation systems. Once we have a good understanding of those techniques, we will begin building a demo system by loading our training data into the Snowflake Data Cloud.

What are Product Recommendation Systems and Why do They Matter?

Customer Value

Product recommendation systems are software that use machine learning and data analysis techniques to recommend products or services to users based on their past behavior and preferences. These systems can be used to recommend products, services, content, or other items to users based on historical data surrounding these interactions.

Product recommendation systems also help users by providing them with personalized and relevant recommendations that are more likely to be of interest to them. This can lead to increased customer satisfaction, as users will be more likely to find and interact with products that they are interested in. 

Additionally, it can save the user time and effort in finding the right product for them. These benefits also increase customer loyalty, as users will be more likely to return to the business to find and purchase more products that they are interested in.

The Business Proposition

This leads us to the main purpose of product recommendation systems: business development. 

These systems help businesses and organizations increase engagement, sales, and revenue by providing personalized and relevant recommendations to their customers. 

By providing recommendations that are tailored to each user’s preferences and behavior, these systems can help businesses increase the number of sales and the average value of each sale, which ultimately leads to an increase in revenue.

Furthermore, product recommendation systems can also increase revenue by helping businesses to better target their marketing efforts. By providing insights into the preferences and behavior of customers, these systems can help businesses to create more effective marketing campaigns that are more likely to resonate with their target audience.

In today’s highly competitive business environment, product recommendation systems are becoming increasingly important. With the explosion of data and the growing importance of personalization, businesses must be able to make sense of this data in order to create personalized and relevant recommendations for their customers. 

As such, product recommendation systems are increasingly being used by businesses of all sizes to increase customer engagement, sales, and revenue.

Popular Examples of Product Recommendation Systems

One of the most well-known examples of product recommendation systems is Netflix. Netflix stores data about the movies and TV shows that users have watched in the past, as well as their ratings, and then uses that information to recommend similar movies and TV shows that they may be interested in. 

Netflix also takes into account other information like the user’s location, device type, and time of day to improve these recommendations.

Another example is the e-commerce website, Amazon. This marketplace uses demographic information to provide personalized product recommendations to its customers. The system also takes into account the products that users have viewed, added to their cart, and purchased in the past, as well as their browsing and search history. 

Amazon also uses demographic information such as the user’s location, age, and browsing history to make recommendations. 

For our final example, let us consider Spotify, the popular music streaming service. Features like the Discover Weekly and Release Radar playlists, which are personalized playlists that are updated every week, provide users with new and relevant music based on their listening history and preferences. 

These insights are driven by information such as genre, artist, and album as well as audio features like tempo, energy, and valence. The system also analyzes the lyrics of the songs to understand the content and provide better recommendations to users.

What Are The Techniques Used In Recommendation Systems?

Product recommendation systems can be divided into two main categories: Collaborative Filtering and Content-Based Filtering.

Collaborative Filtering

Collaborative Filtering systems use the past behavior of a group of users to make recommendations for a specific user. They look for users similar to the target user and recommend items that those similar users have liked.

Content-Based Filtering

Content-Based Filtering systems use the characteristics or features of the items themselves to make recommendations. They recommend items that are similar to the ones the target user has liked based on features like genre, author, or description.

In practice, recommendation systems often use a combination of both Collaborative Filtering and Content-Based Filtering, known as Hybrid Recommendations.

Hybrid Recommendation

Hybrid Recommendation combines the strengths of both the methods by taking into account the behavior of similar users and the characteristics of the items.

Practical Example of Recommendation Systems

To better understand these two techniques, let us return to the Netflix example.. Netflix uses a hybrid system of Collaborative Filtering and Content-Based Filtering to make recommendations based on observations from user behavior. 

Below is an architecture diagram representing a high level overview of Netflix’s recommendation system.

You will notice that Movies are assigned ratings by users. These ratings are key to determine how one user might value a movie relative to their peer’s preferences. For example, if my friends highly review Lord of the Rings, I will probably like Lord of the Rings. To process these ratings, we use Collaborative Filtering. 

Under the hood, Collaborative Filtering is based on a technique called matrix factorization.

Matrix Factorization

Matrix Factorization is a mathematical technique that is used to break down a large matrix into smaller, simpler matrices. This process is done by finding a set of factors that when multiplied together, produce the original matrix. There are several factorization algorithms but let us assume that we use the Singular Value Decomposition algorithm or SVD.

Singular Value Decomposition

Singular Value Decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any M x N  matrix. 

Now that we have reviewed the underlying statistical knowledge, let’s return to our Collaborative Filtering component for movie recommendations. First, we consume user-item interactions data, such as movie ratings, to factorize the user-item matrix into two matrices: one representing the users, and one representing the items. Then, the resulting matrices from factorization are used to find the nearest neighbors in the low-dimensional space, providing recommendations based on the users’ past behavior, and the movies/shows they liked. 

The shorter the euclidean distance i.e. the closer one movie is to another, the more relevant the recommendation. 

The second component of Netflix’s recommendation system incorporates the Content-Based Filtering approach which utilizes the characteristics of the movies and TV shows themselves to make recommendations. This includes information such as genre, director, actors, and keywords from the show’s description. 

The system also analyzes the video and audio content of the show to understand the characteristics of the show. In order to measure similarity, we can use a technique called Cosine Similarity. This value measures the similarity between two items that ranges between -1 and 1. A value of 1 indicates that the two items are identical, while a value of -1 indicates that they are completely dissimilar. 

Now that we have a good knowledge base, let’s begin building our system!

Snowflake for Recommender Systems

When building systems for recommendations, we need to select platforms that are performant as well as cost effective and meet the needs of a machine learning solution. One such provider is Snowflake.

Snowflake is a data cloud platform that provides data and machine learning tools to build value added AI solutions. 

With Snowflake, you can store and manage all your data in one place, making it easier to collect, clean, and prepare data for machine learning. You can also easily scale up or down based on your data volume and computational needs, making it very cost-effective.

Furthermore, Snowflake provides native support for machine learning algorithms and tools. You can build, train, and validate machine learning models directly in Snowflake using the Snowpark SDK for Java, Python, or Scala with incredible performance

Or for a more diverse approach, Snowflake also integrates with popular platforms such as AWS for seamless multi platform solutions. To that end, let’s look at a Snowflake tutorial for a recommender system.

Recommender Tutorial

The engineering team at Snowflake has provided a practical example utilizing the MovieLens data set. This is a classic for experimenting with recommendation systems. Our goal will be to build a system that can provide users suggestions for films they might enjoy.

We will be using Snowpark to ingest and host our data and then feeding that data AWS Sagemaker for training and inference. 

This link will redirect you to the Snowflake quickstart tutorial. Please expect the tutorial to take roughly 1.5 hours for completion. Have fun!

Closing Thoughts

Congrats! You now have a good working knowledge of recommendation systems and even built a simple application with Snowflake and AWS!

Still curious?

Reach out to phData and see how our consultants can help your business grow with our machine learning and Snowflake consulting services.

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