Create personalized movie recommendations

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In this tutorial, we will use the Movielens dataset to demonstrate how to upload your product catalog and user events into Vertex AI Search for retail and train a personalized product recommendation model. The Movielens dataset contains a catalog of movies (products) and user movie ratings (user events).

We will treat each positive movie rating (rating >= 4) as a product page view event. We will train a recommendation model of type Others You May Like that will make movie recommendations based on any user or a seed movie in our dataset.

Estimated time:

• Initial steps to start training the model: ~1.5 hours.
• Waiting for the model to train: ~2 days.
• Evaluating the model predictions and cleaning up: ~30 minutes.

Objectives

• Learn how to import products and user events data from BigQuery into Vertex AI Search for retail.
• Train and evaluate recommendation models.

Costs

This tutorial uses billable components of Google Cloud, including:

• Cloud Storage
• BigQuery
• Vertex AI Search for retail

For more information about Cloud Storage costs, see the Cloud Storage pricing page.

For more information about BigQuery costs, see the BigQuery pricing page.

For more information about Vertex AI Search for retail costs, see the Vertex AI Search for retail pricing page.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Google Cloud project.

Prepare the dataset

Open Google Cloud console, select your Google Cloud project. Take note of the project ID in the Project info card on the dashboard page. You will need the project ID for the following steps. Next, click the Activate Cloud Shell button at the top of the Console.

A Cloud Shell session opens inside a new frame at the bottom of the Google Cloud console and displays a command-line prompt.

Import the dataset

1. Using the Cloud Shell, download and unpack the source dataset:

   wget https://files.grouplens.org/datasets/movielens/ml-latest.zip
   unzip ml-latest.zip
   

2. Create a Cloud Storage bucket and upload the data into it:

   gcloud storage buckets create gs://PROJECT_ID-movielens-data
   gcloud storage cp ml-latest/movies.csv ml-latest/ratings.csv \
     gs://PROJECT_ID-movielens-data
   

3. Create a BigQuery dataset:

   bq mk movielens
   

4. Load movies.csv into a new movies BigQuery table:

   bq load --skip_leading_rows=1 movielens.movies \
     gs://PROJECT_ID-movielens-data/movies.csv \
     movieId:integer,title,genres
   

5. Load ratings.csv into a new ratings BigQuery table:

   bq load --skip_leading_rows=1 movielens.ratings \
     gs://PROJECT_ID-movielens-data/ratings.csv \
     userId:integer,movieId:integer,rating:float,time:timestamp
   

Create BigQuery views

1. Create a view that converts the movies table into the retail product catalog schema:

   bq mk --project_id=PROJECT_ID \
    --use_legacy_sql=false \
    --view '
    SELECT
      CAST(movieId AS string) AS id,
      SUBSTR(title, 0, 128) AS title,
      SPLIT(genres, "|") AS categories
    FROM `PROJECT_ID.movielens.movies`' \
   movielens.products
   

   Now the new view has the schema that Vertex AI Search for retail expects. Then, from the left sidebar, choose BIG DATA -> BigQuery. Then, from the explorer bar on the left, expand your project name and select movielens -> products to open the query page for this view.

   

2. Now let's convert movie ratings into user events. We will:

   • Ignore negative movie ratings (<4)
   • Treat every positive rating as a product page view event (detail-page-view)
   • Rescale the Movielens timeline into the last 90 days. We do this for two reasons:
     • Vertex AI Search for retail requires that user events are no older than 2015. Movielens ratings go back to 1995.
     • Vertex AI Search for retail uses the last 90 days of user events when serving prediction requests for a user. Every user will appear to have recent events when we make predictions for any user later on.

   Create a BigQuery view. The following command uses a SQL query that meets conversion requirements listed above.

   bq mk --project_id=PROJECT_ID \
    --use_legacy_sql=false \
    --view '
    WITH t AS (
      SELECT
        MIN(UNIX_SECONDS(time)) AS old_start,
        MAX(UNIX_SECONDS(time)) AS old_end,
        UNIX_SECONDS(TIMESTAMP_SUB(
          CURRENT_TIMESTAMP(), INTERVAL 90 DAY)) AS new_start,
        UNIX_SECONDS(CURRENT_TIMESTAMP()) AS new_end
      FROM `PROJECT_ID.movielens.ratings`)
    SELECT
      CAST(userId AS STRING) AS visitorId,
      "detail-page-view" AS eventType,
      FORMAT_TIMESTAMP(
        "%Y-%m-%dT%X%Ez",
        TIMESTAMP_SECONDS(CAST(
          (t.new_start + (UNIX_SECONDS(time) - t.old_start) *
            (t.new_end - t.new_start) / (t.old_end - t.old_start))
        AS int64))) AS eventTime,
      [STRUCT(STRUCT(movieId AS id) AS product)] AS productDetails,
    FROM `PROJECT_ID.movielens.ratings`, t
    WHERE rating >= 4' \
   movielens.user_events
   

Import product catalog and user events

We are now ready to import the product catalog and the user event data into Vertex AI Search for retail.

1. Enable the Vertex AI Search for retail API for your Google Cloud project.

   ENABLE THE API

2. Click Get started.

3. Go to the Data> page in the Search for Retail console.
   
   Go to the Data page

4. Click Import.

Import product catalog

1. Fill in the form to import products from the BigQuery view you created above:

   • Select import type: Product Catalog.
   • Select the default branch name.
   • Select source of data: BigQuery.
   • Select schema of data: Retail Product Schema.

   • Enter the name of the products BigQuery view you created above (PROJECT_ID.movielens.products).

     Note: The BigQuery view name is obtained from the view ID by replacing the colon (:) with a period (.). You can find the view ID on the BIG DATA -> BigQuery page by selecting your view from the left menu and going to the DETAILS tab.

2. Click Import.

   Note: It may take a few minutes for Vertex AI Search for retail API to fully activate and you may get an error in this step if the activation has not finished. If that happens, try again a few minutes later.

3. Wait until all products have been imported, which should take 5–10 minutes.

   You can check the import activity for the import operation status. When the import is complete, the import operation status changes to Succeeded.

   

Import user events

1. Import the user_events BigQuery view:

   • Select import type: User Events.
   • Select source of data: BigQuery.
   • Select schema of data: Retail User Events Schema.
   • Enter the name of the user_events BigQuery view you created above.

2. Click Import.

3. Wait until at least a million events have been imported before proceeding to the next step, in order to meet the data requirements for training a new model.

   You can check the import activity for the operation status. The process takes about an hour to complete.

   

Train and evaluate recommendation models

Create a recommendation model

1. Go to the Models page in the Search for Retail console.
   
   Go to the Models page

2. Click Create model:

   • Give the model a name.
   • Select Others you may like as the model type.
   • Choose Click-through rate (CTR) as the business objective.

3. Click Create.

   

   Your new model starts training.

   

Create a serving config

1. Go to the Serving Configs page in the Search for Retail console.
   
   Go to the Serving configs page

2. Click Create serving config:

   • Select Recommendation.
   • Give the serving config a name.
   • Select the model you created.

3. Click Create.

Wait for the model to be "Ready to query"

It takes about two days for the model to train and become ready to query.

To view the status, click the created serving config on the Serving configs page.

The Model ready to query field indicates Yes when the process is complete.

Preview recommendations

Once the model is ready to query:

1. Go to the Serving Configs page in the Search for Retail console.
   
   Go to the Serving configs page
2. Click the serving config name to go to its detail page.
3. Click the *Evaluate tab.

4. Enter a seed movie ID, such as 4993 for "The Lord of the Rings: The Fellowship of the Ring (2001)".

   

5. Click Prediction preview to see the list of recommended items on the right of the page.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

Delete individual resources

1. Go to the Serving configs page and delete the serving config you created.

2. Go to the Models page and delete the model.

3. Delete the BigQuery dataset in Cloud Shell:

   bq rm --recursive --dataset movielens
   

4. Delete the Cloud Storage bucket and its contents:

   gcloud storage rm gs://PROJECT_ID-movielens-data --recursive
   

What's next

• Implementing Vertex AI Search for retail