Overview

This lab shows you how to use low-level TensorFlow commands to do content-based filtering.

Objectives

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

• Create and compute a user feature matrix.
• Compute where each user lies in the feature embedding space.
• Create recommendations for new movies based on similarity measures between the user and movie feature vectors.

Introduction

In this lab, you provide movie recommendations for a set of users. Content-based filtering uses features of the items and users to generate recommendations. In this small example, you use low-level TensorFlow operations and a very small set of movies and users to illustrate how this occurs in a larger content-based recommendation system.

Task 1. Setup

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 Google Skills 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.

Enable the Vertex AI API

1. In the Google Cloud Console, on the Navigation menu, click Vertex AI.
2. Click Enable Vertex AI API.

Enable the Notebooks API

1. In the Google Cloud Console, on the Navigation menu, click APIs & Services > Library.
2. Search for Notebooks API, and press ENTER.
3. Click on the Notebooks API result.
4. If the API is not already enabled, click Enable.

Task 2. Launch a Vertex AI Notebooks instance

1. In the Google Cloud Console, on the Navigation Menu, click Vertex AI > Workbench. Select User-Managed Notebooks.

2. On the Notebook instances page, Click Create New and choose the latest version of TensorFlow Enterprise 2.6 (with LTS) in Environment.

3. In the New notebook instance dialog, confirm the name of the deep learning VM, if you don’t want to change the region and zone, leave all settings as they are and then click Create. The new VM will take 2-3 minutes to start.

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

Task 3. Clone a course repo within your Vertex AI Notebooks instance

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Task 4. Implement a content-based filtering using low level tensorflow operations

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > labs, and open content_based_by_hand.ipynb.

2. In the notebook interface, click Edit > Clear All Outputs.

3. Carefully read through the notebook instructions, and fill in lines marked with #TODO where you need to complete the code.

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you. Highlight the text to read the hints, which are in white text.
• To view the complete solution, navigate to training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > solutions, and open content_based_by_hand.ipynb.

End your lab

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

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