Overview

This lab shows you how to create a hybrid recommendation system using a combination of approaches and a neural network.

Objectives

In this lab, you learn to perform the following tasks:

• Generate content-based features from our content-based recommendor

• Create learned embeddings from our collaborative filtering system based on WALS

• Combine the two different systems within a single deep neural network

Introduction

In this lab, you'll be providing article recommendations for users based on the Kurier.at data. You'll be combining both the content-based and collaborative filtering systems you've developed in previous labs.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Activate Cloud Shell

Cloud Shell is a virtual machine that contains development tools. It offers a persistent 5-GB home directory and runs on Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab completion.

1. Click the Activate Cloud Shell button () at the top right of the console.

2. Click Continue.
   It takes a few moments to provision and connect to the environment. When you are connected, you are also authenticated, and the project is set to your PROJECT_ID.

Sample commands

• List the active account name:

gcloud auth list

(Output)

Credentialed accounts: - <myaccount>@<mydomain>.com (active)

(Example output)

Credentialed accounts: - google1623327_student@qwiklabs.net

• List the project ID:

gcloud config list project

(Output)

[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: Full documentation of gcloud is available in the gcloud CLI overview guide.

Task 1. Create storage bucket

Duration is 2 min

1. In the Google Cloud Console, on the Navigation menu (), click Cloud Storage.

2. Click Create bucket.

3. Type a unique name, such as your project ID.

4. Click Create.

Task 2. Launch AI Platform Notebooks

To launch AI Platform Notebooks:

Step 1

Click on the Navigation Menu. Navigate to AI Platform, then to Notebooks.

Step 2

On the Notebook instances page, click . Select a 1.XX version of TensorFlow (not a 2.0) without GPUs. In the following example, you would select Tensorflow Enterprise 1.15 > Without GPUs:

Tensorflow 1.XX versions change semi-frequently, so the version you pick may be different.

In the pop-up, confirm the name of the deep learning VM and click Create.

The new VM will take 2-3 minutes to start.

Step 3

Click Open JupyterLab. A JupyterLab window will open in a new tab.

Task 3. Clone course repo within your AI Platform Notebooks instance

To clone the training-data-analyst notebook in your JupyterLab instance:

1. In JupyterLab, to open a new terminal, click the Terminal icon.

2. At the command-line prompt, run the following command:

   git clone https://github.com/GoogleCloudPlatform/training-data-analyst

3. To confirm that you have cloned the repository, double-click on the training-data-analyst directory and ensure that you can see its contents.
   The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Task 4. Create a hybrid recommendation system

1. In the notebook interface, navigate to datalab > notebooks > training-data-analyst > courses > machine_learning > deepdive > 10_recommend > labs > hybrid_recommendations > hybrid_recommendations.ipynb.

2. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

The number of stars indicates the following:

• 1 star = Very dissatisfied
• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

You can close the dialog box if you don't want to provide feedback.

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