How to Develop Recommendation System with AWS Personalize

Quick Summary

In this tutorial, you will learn how to develop recommendation system with AWS Personalize using a simple step-by-step approach. We have covered everything from how to upload your dataset to build, train, and test your personalized model. You will learn about the different recommendation techniques, like collaborative filtering, and see how AWS Personalize simplifies the machine learning pipeline with ready-to-use infrastructure and minimal coding.

Introduction

Personalized recommendations have become a key part of today’s modern digital experience. Whether an online store suggests products or a video app recommends what to watch next, users now expect content that matches their interests.

However, building a recommendation system from scratch usually involves complex coding, large datasets, and machine learning, which is time-consuming and substantially difficult. This is where AWS Personalize comes in. It is a fully managed service by AWS that helps you with this issue without ML expertise and does all the heavy lifting for you.

Explore how to develop recommendation system with AWS Personalize, learn how you can upload your dataset to generate live recommendations, all with minimal code setup. Let’s begin with understanding the basics first.

What is a Recommendation System?

A recommendation is an AI-powered tool that uses an algorithm to suggest items to users based on their past behaviour and preferences. It analyzes data, like what a user has viewed or liked, and predicts what other items they may enjoy. This helps personalize user experiences on platforms like Amazon, Netflix, and Spotify.

Types of Recommendation Approaches

1. Content-based filtering: Recommends items similar to what a user has liked before, based on item features.
2. Collaborative filtering: Makes recommendations based on how similar users have interacted with items.

User-based: Users similar to you have liked these items
Item-based: Users who liked this item also liked these other items

3. Hybrid approaches: Combines multiple techniques to overcome the limitations of any single approach.
4. Contextual recommendations: Takes into account contextual information like time, location, or device.
5. Session-based recommendations: Make recommendations based on a user’s current session behavior rather than long-term history.

What is AWS Personalize?

AWS Personalize is a fully managed machine learning service by Amazon Web Services that helps you build personalized recommendation systems without the need for expertise in ML. Instead, it uses machine learning algorithms to analyze data, such as user behaviour, and help you generate real-time personalized recommendations.

Why Use AWS Personalize?

Traditionally, building recommendation systems requires handling:

Feature engineering
Model selection
Infrastructure setup
Real-time inference pipelines

AWS Personalize resolves these complexities. Instead of dealing with complicated coding and setups, you just make a few simple requests through API calls. Simply upload your data, train the system to understand it, and start receiving personalized recommendations for your users, without having to manage everything as your user base grows.

How it Works

Here’s how AWS Personalize works:
1. Data Collection: First, it collects data like interactions and preferences.
2. Data preparation: Then it prepares the data by uploading it to S3
3. Model Training: Next, it trains the models using pre-built algorithms to personalize recommendations.
4. Deployment: Then it deploys the trained models via APIs for real-time recommendations.
5. Continuous Optimization: Finally, it continuously updates the data and retrains the model to improve recommendations.

Steps to Develop Recommendation System with AWS Personalize

Here is how to develop recommendation system with AWS Personalize, in a few simple steps.

AWS services used in the process:
1. S3
2. AWS Personalized
3. IAM

Step1: Create S3 Bucket & Upload Dataset

To begin, we need an S3 bucket to store the dataset that will be used by AWS Personalize.

1. Go to the S3 bucket
2. Create a bucket personalize-demo-bac
3. Upload your interactions.csv file
4. Interactions.csv (Need to enter at least 1000 data)

Note the path:
s3://personalize-demo-bac/interactions.csv

Step 2: Set Bucket Permissions for Personalize

Next, configure the permissions so AWS Personalize can access the data in your S3 bucket.

Navigate to your S3 bucket and select the permissions tab.
Click on the Bucket Policy and paste the following policy.

{
	"Version": "2012-10-17",
	"Statement": [
    	{
        	"Sid": "AllowPersonalizeReadAccess",
        	"Effect": "Allow",
        	"Principal": {
            	"Service": "personalize.amazonaws.com"
        	},
        	"Action": "s3:GetObject",
        	"Resource": "arn:aws:s3:::personalize-demo-bac/*"
    	},
    	{
        	"Sid": "AllowPersonalizeListBucket",
        	"Effect": "Allow",
        	"Principal": {
            	"Service": "personalize.amazonaws.com"
        	},
        	"Action": "s3:ListBucket",
        	"Resource": "arn:aws:s3:::personalize-demo-bac"
    	}
	]
}

Step 3: Create a Dataset Group

In this step, you’ll create a dataset group in AWS Personalize, where your data will be stored and organized.

1. Go to the AWS Personalize console
2. Click “Dataset groups” > Create dataset group
3. Name: demo-dataset-bac
4. Domain: Custom
5. Click on Create Dataset

Step 4: Create Datasets and Import Data

Now, you’ll define the dataset structure (schema) and import your interaction data for training.

1. Click on Dropdown-> Item Interactions dataset-required

2. Choose option 1 and click Next.

3. Enter Dataset name: my-dataset-bac
Create a new schema
Schema name: demo-personlalized-bac

4. And enter the schema field and click on Next.

{
   "type": "record",
   "name": "Interactions",
   "namespace": "com.amazonaws.personalize.schema",
   "fields": [
       {
           "name": "USER_ID",
           "type": "string"
       },
       {
           "name": "ITEM_ID",
           "type": "string"
       },
       {
           "name": "EVENT_TYPE",
           "type": "string"
       },
       {
           "name": "TIMESTAMP",
           "type": "long"
       }
   ],
   "version": "1.0"
}

5. Now import the job
Dataset import job: my-dataset-job
Data location: s3://personalize-demo-bac/interactions.csv

6. Attach one IAM Role and click on Start Import.

7. You will be able to see this message after completing the whole import data process.

Step 5: Run Data Analysis

This step will analyze your data to identify patterns and provide insights for recommendation algorithms.

Click on Run data analysis
Wait for a few minutes for the analysis to complete

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Hire AWS developers to streamline your data pipeline, fine-tune models, and ensure your system delivers, high-performance recommendations.

Step 6: Set up Training and Recommendation Resources

Now you will configure the solution and choose the best algorithm for generating recommendations.

Click on Create solution
Solution name: demo-solution-bac-personalized
Solution type: Item recommendation
Recipe: aws-similar item (Here, based on our dataset, we need to choose the solution type and recipe)
Click on Next and proceed with the default settings to create the solution.

Keep other things as it is and next, create a solution

Step 7: Create a Solution Version

Once your solution is created, this step allows you to create a specific version of the recommendation model.

First, click on Create solution version.

Give the solution version Name: first.
Click on create

Note: It will take time to create a version.

Step 8: Create a Campaign

A campaign allows you to deploy your trained model and start receiving live recommendations.

Click on Create campaign.

Campaign name: demo-campaign
Solution: Click on the dropdown and choose the previously created solution(demo-solution-bac-personalized)
Click on Create campaign.

Here, a campaign is created.

Step 9: Test the Campaign Result

In this final step, you can test the recommendation system and verify that your model is generating accurate results.

To verify whether the recommendation is working properly:

Enter Item ID: (e.g., user88)
Click on Get recommendation.

After that, we will recommend item numbers.

Conclusion

By following this tutorial, you have learned how to develop recommendation system with AWS Personalize, building personalized experiences without the need for deep machine learning expertise. From uploading your data to testing live campaigns, you’ve seen how easy it can be to create a powerful, functional recommendation system that demonstrates the value of automation and machine learning in enhancing user experiences.

However, if you want to ensure your recommendation system is optimized for production or need help scaling it, consider opting for AWS consulting services to speed up the process and avoid common roadblocks.