Overview

The matrix factorization approach does not use any information about users or movies beyond what is available from the ratings matrix. However, we will often have user information (such as the city they live, their annual income, their annual expenditure, etc.) and we will almost always have more information about the products in our catalog. How do we incorporate this information in our recommendation model?

The answer lies in recognizing that the user factors and product factors that result from the matrix factorization approach end up being a concise representation of the information about users and products available from the ratings matrix. We can concatenate this information with other information we have available and train a regression model to predict the rating.

Objectives

In this lab, you will:

• Know how to extract user and product factors from a BigQuery Matrix Factorizarion Model
• Know how to format inputs for a BigQuery Hybrid Recommendation Model

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.

Task 1. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 2. Clone a course repo within your JupyterLab interface

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.

Click Check my progress to verify the objective. Clone a course repo within your JupyterLab interface

Task 3. Hybrid recommendations with the Movie Lense dataset

Duration is 60 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > labs > als_bqml_hybrid.ipynb. ` In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.

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

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

Tip: To run the current cell you can click the cell and hit shift + enter. Other cell commands are found in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
• If you need more help, you may take a look at the complete solution by navigating to training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > solutions and opening als_bqml_hybrid.ipynb.

End your lab

When you have completed your lab, click End Lab. Google Cloud Skills Boost 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.

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

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