Get started with media recommendations

Get started with media recommendations

You can quickly build a state-of-the-art media recommendations app. Media recommendations enable your audiences to discover more personalized content, like what to watch or read next, with Google-quality results that are customized by optimization objectives.

For general information about Vertex AI Search for media, see Introduction to media search and recommendations.

In this getting-started tutorial, you will use the Movielens dataset to demonstrate how to upload your media content catalog and user events into Vertex AI Search and train a personalized movie recommendation model. The Movielens dataset contains a catalog of movies (documents) and user movie ratings (user events).

In this tutorial, you train a recommendation model of type Others You May Like optimized for click-through-rate (CTR). After training, the model can recommend movies based on a user ID and on a seed movie.

To meet the minimum data requirements for the model, each positive movie rating (4 or higher) is treated as a view-item event.

Estimated time to complete this tutorial:

• Initial steps to start training the model: ~1.5 hours.
• Waiting for the model to train: ~24 hours. (Train the model)
• Evaluating the model predictions and cleaning up: ~30 minutes. (Preview recommendations)

If you completed the Get started with media search tutorial and you still have the data store (suggested name quickstart-media-data-store), then you can use that data store instead of creating another. In this case, you should begin the tutorial at Create an app for media recommendations.

Objectives

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

Before following this tutorial, make sure you have done the steps in Before you begin.

---

To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:

Guide me

---

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. Enable the Vertex AI Agent Builder, Cloud Storage, BigQuery APIs.

   Enable the APIs

5. 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

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

7. Enable the Vertex AI Agent Builder, Cloud Storage, BigQuery APIs.

   Enable the APIs

Prepare the dataset

You use the Cloud Shell to import the Movielens dataset and restructure the dataset for Vertex AI Search for media.

Open the Cloud Shell

1. Open Google Cloud console.
2. Select your Google Cloud project.
3. Take note of the project ID in the Project info card on the dashboard page. You will need the project ID for the following procedures.

4. 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

The Movielens dataset is available in a public Cloud Storage bucket to make it easier to import.

1. Run the following using your project ID to set the default project for the command-line.

   gcloud config set project PROJECT_ID
   

2. Create a BigQuery dataset:

   bq mk movielens
   

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

   bq load --skip_leading_rows=1 movielens.movies \
     gs://cloud-samples-data/gen-app-builder/media-recommendations/movies.csv \
     movieId:integer,title,genres
   

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

   bq load --skip_leading_rows=1 movielens.ratings \
     gs://cloud-samples-data/gen-app-builder/media-recommendations/ratings.csv \
     userId:integer,movieId:integer,rating:float,time:timestamp
   

Create BigQuery views

In this step, you will restructure the Movielens dataset so it follows the expected format for media recommendations. Media recommendations require user events data in order to create a model. For this guide, you will create fake view-item events during the past 90 days from positive ratings (< 4).

1. Create a view that converts the movies table into the Google-defined Document schema:

   bq mk --project_id=PROJECT_ID \
    --use_legacy_sql=false \
    --view '
     WITH t AS (
       SELECT
         CAST(movieId AS string) AS id,
         SUBSTR(title, 0, 128) AS title,
         SPLIT(genres, "|") AS categories
         FROM `PROJECT_ID.movielens.movies`)
       SELECT
         id, "default_schema" as schemaId, null as parentDocumentId,
         TO_JSON_STRING(STRUCT(title as title, categories as categories,
         CONCAT("http://mytestdomain.movie/content/", id) as uri,
         "2023-01-01T00:00:00Z" as available_time,
         "2033-01-01T00:00:00Z" as expire_time,
         "movie" as media_type)) as jsonData
       FROM t;' \
   movielens.movies_view
   

   Now, the new view has the schema that the Vertex AI Agent Builder API expects.

2. Go to the BigQuery page in Google Cloud console.

   Go to BigQuery

3. In the Explorer pane, expand your project name, expand the movielens dataset and click movies_view to open the query page for this view.

   

4. Go to the Table explorer tab.

5. In the Generated query pane, click the Copy to query button. The query editor opens.

6. Click Run to see movie data in the view that you created.

7. Create fictitious user events from movie ratings by running the following Cloud Shell command:

   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 userPseudoId,
       "view-item" 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(movieId AS id, null AS name)] AS documents,
     FROM `PROJECT_ID.movielens.ratings`, t
     WHERE rating >= 4;' \
     movielens.user_events
   

Activate Vertex AI Agent Builder

1. In the Google Cloud console, go to the Agent Builder page.

   Agent Builder

2. Read and agree to the Terms of Service, then click Continue and activate the API.

Create an app for media recommendations

The procedures in this section guide you through creating and deploying a media recommendations app.

1. In the Google Cloud console, go to the Agent Builder page.

   Agent Builder

2. Click Create app.

3. On the Create app page, select Recommendations.

4. In the App name field, enter a name for your app, such as quickstart-media-recommendations. Your app ID appears under the app name.

5. Under Content, select Media.

6. Under Recommendations type, make sure Others you may like is selected.

7. Under Business Objective, make sure Click-through rate (CTR) is selected.

8. Click Continue.

9. If you completed the Get started with media search tutorial and you still have the data store (suggested name quickstart-media-data-store), then select it, click Create, and skip to Train the recommendation model.

10. On the Data Stores page, click Create data store.

11. Enter a display name for your data store, such as quickstart-media-data-store, and then click Create.

12. Select the data store you just created, and then click Create to create your app.

Import data

Next, import the movies and user events data that were formatted earlier.

Import documents

1. Open the Requirements tab.

2. Click Import Documents.

3. Select BigQuery.

4. Enter the name of the movies BigQuery view you created and click Import.

   PROJECT_ID.movielens.movies_view
   

5. Wait until all documents have been imported, which should take about 15 minutes. There should be 86537 documents when complete.

   You can check the Activity tab for the import operation status. When the import is complete, the import operation status changes to Completed.

Import user events

1. Open the Requirements tab.

2. Click Import Events.

3. Select BigQuery.

4. Enter the name of the user_events BigQuery view you created and click Import.

   PROJECT_ID.movielens.user_events
   

5. 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 Activity tab for the operation status. The process takes about an hour to complete because you are importing millions of rows.

   It can take some time for the Requirements tab to update its status to Data requirements met.

Train the recommendation model

1. Go to the Configurations page.

2. Click the Serving tab. A serving config has already been created.

   If you want to adjust the Recommendation demotion or Result diversification settings, you can do so on this page.

3. Click the Training tab.

   After the data requirements have been met, the model begins training automatically. You can view the training and tuning status on this page.

   It might take a couple of days for the model to train and become ready to query. The Ready to query field indicates Yes when the process is complete. You need to refresh the page to see the change No to Yes.

Preview recommendations

After the model is ready to query:

1. In the navigation menu, click Preview.

2. Click the Document ID field. A list of document IDs appears.

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

   

4. Select the Serving config name from the drop-down menu.

5. Click Get recommendations. A list of recommended documents appears.

Deploy your app for structured data

There is no recommendations widget for deploying your app. To test your app before deployment:

1. Go to the Data page, Documents tab, and copy a document ID.

2. Go to the Integration page. This page includes a sample command for the servingConfigs.recommend method in the REST API.

3. Paste the document ID you copied earlier into the Document ID field.

4. Leave the User Pseudo ID field as is.

5. Copy the example request and run it in Cloud Shell.

For help integrating the recommendations app into your web app, see the code samples at Get media recommendations.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

You can reuse the data store you created for media search in the Get started with media search tutorial. Try that tutorial before doing this clean up procedure.

1. To avoid unnecessary Google Cloud charges, use the Google Cloud console to delete your project if you don't need it.
2. If you created a new project to learn about Vertex AI Agent Builder and you no longer need the project, delete the project.
3. If you used an existing Google Cloud project, delete the resources you created to avoid incurring charges to your account. For more information, see Delete an app.
4. Follow the steps in Turn off Vertex AI Agent Builder.

5. If you created a BigQuery dataset, delete it in Cloud Shell:

   bq rm --recursive --dataset movielens
   

What's next

• Introduction to Vertex AI Search
• About apps and data stores