Create a recommendation engine for your website with AWS SageMaker
Tags: machinelearning, webdev, aws, beginners
Published: 2022-12-15 09:32:00


Online businesses often face the challenge of how to effectively personalize their products and services for individual users. One way to do this is to use machine learning to analyze user behaviour and make predictions about what items they are likely to be interested in.

In this blog post, I will show you how to use Amazon SageMaker to build a recommendation engine that can predict what items to offer to a user based on their browsing history. We will use the AWS SDK for JavaScript (Node.js) and the SageMaker SDK for Node.js to train and evaluate the model, and I'll provide examples and code snippets to help you understand the steps involved.

Hopefully, you can at least get the idea about how to add such a functionality to your website and you get the keywords to follow and to get started in using ML to take your applications' experience and functionality to a whole new level.

> Before we move on, remember you can implement your websites or landing pages **with or without** coding on [DoTenX](https://dotenx.com) for **free**. Make sure to check it out and instead of focusing on the boring parts, spend your time building a better application.

I assume you have access to AWS and have a basic understanding of how to navigate the console. I intentionally avoid going into every details and my goal is to give you an idea about how you can use ML easily to add new functionalities to your application.

Let's assume we want to offer some items on a clothing website based on the items the user has liked before. To implement this recommendation system, you can follow these steps:

1. First, you would need to collect data on the clothing items that users have viewed on your website. This data could include the user's unique identifier, the time and date that they viewed the item, the item's unique identifier, and the item's category (such as "dresses" or "shirts").

2. Next, you would need to format this data as a CSV file and upload it to an Amazon S3 bucket.

user_id,item_id,timestamp,category

123,456,2022-12-15T12:00:00,dresses

123,789,2022-12-15T13:00:00,shirts

456,789,2022-12-15T14:00:00,dresses

3.Then, you would need to create a new SageMaker notebook instance, which will be used to train and evaluate the machine learning model.

4. In the notebook, import the AWS SDK and SageMaker module. Next, you would need to specify the S3 bucket and file that contain the training data, and then use the getObject() method to download the data and load it into a JavaScript array.

``` javascript
// Import the AWS SDK for JavaScript
const AWS = require('aws-sdk')

// Import the SageMaker SDK for Node.js
const SageMaker = require('aws-sdk/clients/sagemaker')

// Specify the S3 bucket and file that contain the training data
const bucket = 'my-s3-bucket'
const dataKey = 'user-item-data.csv'

// Use the getObject() method to download the data
const s3 = new AWS.S3()
const data = await s3.getObject({
  Bucket: bucket,
  Key: dataKey
}).promise()

// Load the data into a JavaScript array
const rows = data.Body.toString('utf-8').split('\n')
const items = rows.map(row => row.split(','))
```
5. Then, you would need to preprocess the data to prepare it for training the model. This could include tasks such as cleaning and transforming the data, and creating features that will be used by the model.

``` javascript
// Preprocess the data to prepare it for training
const cleaned = items.filter(item => item.length === 4)
const transformed = cleaned.map(item => ({
  userId: item[0],
  itemId: item[1],
  timestamp: item[2],
  category: item[3]
}))
```
6. Once the data is prepared, you can use the SageMaker SDK for Node.js to create a recommendation model and train it using the data.

``` javascript
// Create a recommendation model and train it
const model = new SageMaker.FactorizationMachine(
  userId: 'userId',
  itemId: 'itemId',
  predictorType: 'binary_classifier',
  numFactors: 10
)

await model.train(transformed)
```
7. After the model is trained, you can use it to make predictions about what clothing items to offer to a user based on their browsing history.

``` javascript
// Use the model to make predictions about what items to offer
const userId = '123'
const category = 'dresses'
const predictions = await model.predict({
  userId,
  itemId: [],
  context: {
    category
  }
})
```
In this example, we are using the context parameter to specify the category of clothing that we want to recommend to the user (in this case, "dresses"). The model will use this information, along with the user's browsing history, to make predictions about which specific clothing items to offer to the user.

You can adjust the parameters and code to suit your needs, and you can also use other SageMaker features and services, such as hyperparameter tuning and model hosting, to further improve the performance of your model.

The `predictions` variable will contain an array of objects, each representing a clothing item that the model predicts would be of interest to the user based on their browsing history and the specified category of clothing.

The objects in the predictions array will have the following structure:
``` json
{
  itemId: '123', // The unique identifier of the clothing item
  score: 0.9, // The model's prediction of how likely the user is to be interested in the item
}
```
You can use the predictions array to display a list of recommended clothing items to the user on your website. For example, you could sort the array by the score property and then display the top n items, where n is the number of items you want to show to the user.

You could also use the predictions array to personalize the user's browsing experience on your website. For example, you could use the itemId property to display the recommended clothing items prominently on the user's home page, or you could use the score property to adjust the order in which the items are displayed.

Overall, the predictions array provides valuable insights into what clothing items the user is likely to be interested in, and you can use this information to enhance their experience on your website.