Create recommendations based on implicit feedback with a matrix factorization model

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This tutorial teaches you how to create a matrix factorization model and train it on the Google Analytics 360 user session data in the public GA360_test.ga_sessions_sample table. You then use the matrix factorization model to generate content recommendations for site users.

Using indirect customer preference information, like user session duration, to train the model is called training with implicit feedback. Matrix factorization models are trained using the Weighted-Alternating Least Squares algorithm when you use implicit feedback as training data.

Important: You must have a reservation in order to use a matrix factorization model. For more information, see Pricing.

Objectives

This tutorial guides you through completing the following tasks:

• Creating a matrix factorization model by using the CREATE MODEL statement.
• Evaluating the model by using the ML.EVALUATE function.
• Generating content recommendations for users by using the model with the ML.RECOMMEND function.

Costs

This tutorial uses billable components of Google Cloud, including:

• BigQuery
• BigQuery ML

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

For more information about BigQuery ML costs, see BigQuery ML pricing.

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.

6. BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, go to

   Enable the BigQuery API.

   Enable the API

Required Permissions

• To create the dataset, you need the bigquery.datasets.create IAM permission.

• To create the connection resource, you need the following permissions:

  • bigquery.connections.create
  • bigquery.connections.get

• To create the model, you need the following permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.connections.delegate

• To run inference, you need the following permissions:

  • bigquery.models.getData
  • bigquery.jobs.create

For more information about IAM roles and permissions in BigQuery, see Introduction to IAM.

Create a dataset

Create a BigQuery dataset to store your ML model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to the BigQuery page

2. In the Explorer pane, click your project name.

3. Click View actions > Create dataset.

   

4. On the Create dataset page, do the following:

   • For Dataset ID, enter bqml_tutorial.

   • For Location type, select Multi-region, and then select US (multiple regions in United States).

     The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

   • Leave the remaining default settings as they are, and click Create dataset.

     

Prepare the sample data

Transform the data from the GA360_test.ga_sessions_sample table into a better structure for model training, and then write this data to a BigQuery table. The following query calculates the session duration for each user for each piece of content, which you can then use as implicit feedback to infer the user's preference for that content.

Follow these steps to create the training data table:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. Create the training data table. In the query editor, paste in the following query and click Run:

   CREATE OR REPLACE TABLE `bqml_tutorial.analytics_session_data`
   AS
   WITH
     visitor_page_content AS (
       SELECT
         fullVisitorID,
         (
           SELECT
             MAX(
               IF(
                 index = 10,
                 value,
                 NULL))
           FROM
             UNNEST(hits.customDimensions)
         ) AS latestContentId,
         (LEAD(hits.time, 1) OVER (PARTITION BY fullVisitorId ORDER BY hits.time ASC) - hits.time)
           AS session_duration
       FROM
         `cloud-training-demos.GA360_test.ga_sessions_sample`,
         UNNEST(hits) AS hits
       WHERE
         # only include hits on pages
         hits.type = 'PAGE'
       GROUP BY
         fullVisitorId,
         latestContentId,
         hits.time
     )
   # aggregate web stats
   SELECT
     fullVisitorID AS visitorId,
     latestContentId AS contentId,
     SUM(session_duration) AS session_duration
   FROM
     visitor_page_content
   WHERE
     latestContentId IS NOT NULL
   GROUP BY
     fullVisitorID,
     latestContentId
   HAVING
     session_duration > 0
   ORDER BY
     latestContentId;

3. View a subset of the training data. In the query editor, paste in the following query and click Run:

   SELECT * FROM `bqml_tutorial.analytics_session_data` LIMIT 5;

   The results should look similar to the following:

   +---------------------+-----------+------------------+
   | visitorId           | contentId | session_duration |
   +---------------------+-----------+------------------+
   | 7337153711992174438 | 100074831 | 44652            |
   +---------------------+-----------+------------------+
   | 5190801220865459604 | 100170790 | 121420           |
   +---------------------+-----------+------------------+
   | 2293633612703952721 | 100510126 | 47744            |
   +---------------------+-----------+------------------+
   | 5874973374932455844 | 100510126 | 32109            |
   +---------------------+-----------+------------------+
   | 1173698801255170595 | 100676857 | 10512            |
   +---------------------+-----------+------------------+
   

Create the model

Create a matrix factorization model and train it on the data in the analytics_session_data table. The model is trained to predict a confidence rating for every visitorId-contentId pair. The confidence rating is created with centering and scaling by the median session duration. Records where the session duration is more than 3.33 times the median are filtered out as outliers.

The following CREATE MODEL statement uses these columns to generate recommendations:

• visitorId—The visitor ID.
• contentId—The content ID.
• rating—The implicit rating from 0 to 1 calculated for each visitor-content pair, centered and scaled.

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, paste in the following query and click Run:

   CREATE OR REPLACE MODEL `bqml_tutorial.mf_implicit`
     OPTIONS (
       MODEL_TYPE = 'matrix_factorization',
       FEEDBACK_TYPE = 'implicit',
       USER_COL = 'visitorId',
       ITEM_COL = 'contentId',
       RATING_COL = 'rating',
       L2_REG = 30,
       NUM_FACTORS = 15)
   AS
   SELECT
     visitorId,
     contentId,
     0.3 * (1 + (session_duration - 57937) / 57937) AS rating
   FROM `bqml_tutorial.analytics_session_data`
   WHERE 0.3 * (1 + (session_duration - 57937) / 57937) < 1;

   The query takes about 10 minutes to complete, after which the mf_implicit model appears in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, you don't see query results.

Get training statistics

Optionally, you can view the model's training statistics in the Google Cloud console.

A machine learning algorithm builds a model by creating many iterations of the model using different parameters, and then selecting the version of the model that minimizes loss. This process is called empirical risk minimization. The model's training statistics let you see the loss associated with each iteration of the model.

Follow these steps to view the model's training statistics:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, expand your project, expand the bqml_tutorial dataset, and then expand the Models folder.

3. Click the mf_implicit model and then click the Training tab

4. In the View as section, click Table. The results should look similar to the following:

   +-----------+--------------------+--------------------+
   | Iteration | Training Data Loss | Duration (seconds) |
   +-----------+--------------------+--------------------+
   |  5        | 0.0027             | 47.27              |
   +-----------+--------------------+--------------------+
   |  4        | 0.0028             | 39.60              |
   +-----------+--------------------+--------------------+
   |  3        | 0.0032             | 55.57              |
   +-----------+--------------------+--------------------+
   |  ...      | ...                | ...                |
   +-----------+--------------------+--------------------+
   

   The Training Data Loss column represents the loss metric calculated after the model is trained. Because this is a matrix factorization model, this column shows the mean squared error.

Evaluate the model

Evaluate the performance of the model by using the ML.EVALUATE function. The ML.EVALUATE function evaluates the predicted content ratings returned by the model against the evaluation metrics calculated during training.

Follow these steps to evaluate the model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, paste in the following query and click Run:

   SELECT
     *
   FROM
     ML.EVALUATE(MODEL `bqml_tutorial.mf_implicit`);

   The results should look similar to the following:

   +------------------------+-----------------------+---------------------------------------+---------------------+
   | mean_average_precision |  mean_squared_error   | normalized_discounted_cumulative_gain |    average_rank     |
   +------------------------+-----------------------+---------------------------------------+---------------------+
   |     0.4434341257478137 | 0.0013381759837648962 |                    0.9433280547112802 | 0.24031636088594222 |
   +------------------------+-----------------------+---------------------------------------+---------------------+
   

   For more information about the ML.EVALUATE function output, see Matrix factorization models.

Get the predicted ratings for a subset of visitor-content pairs

Use the ML.RECOMMEND to get the predicted rating for each piece of content for five site visitors.

Follow these steps to get predicted ratings:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, paste in the following query and click Run:

   SELECT
     *
   FROM
     ML.RECOMMEND(
       MODEL `bqml_tutorial.mf_implicit`,
       (
         SELECT
           visitorId
         FROM
           `bqml_tutorial.analytics_session_data`
         LIMIT 5
       ));

   The results should look similar to the following:

   +-------------------------------+---------------------+-----------+
   | predicted_rating_confidence   | visitorId           | contentId |
   +-------------------------------+---------------------+-----------+
   | 0.0033608418060270262         | 7337153711992174438 | 277237933 |
   +-------------------------------+---------------------+-----------+
   | 0.003602395397293956          | 7337153711992174438 | 158246147 |
   +-------------------------------+---------------------+--  -------+
   | 0.0053197670652785356         | 7337153711992174438 | 299389988 |
   +-------------------------------+---------------------+-----------+
   | ...                           | ...                 | ...       |
   +-------------------------------+---------------------+-----------+
   

Generate recommendations

Use the predicted ratings to generate the top five recommended content IDs for each visitor ID.

Follow these steps to generate recommendations:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. Write the predicted ratings to a table. In the query editor, paste in the following query and click Run:

   CREATE OR REPLACE TABLE `bqml_tutorial.recommend_content`
   AS
   SELECT
     *
   FROM
     ML.RECOMMEND(MODEL `bqml_tutorial.mf_implicit`);

3. Select the top five results per visitor. In the query editor, paste in the following query and click Run:

   SELECT
     visitorId,
     ARRAY_AGG(
       STRUCT(contentId, predicted_rating_confidence)
       ORDER BY predicted_rating_confidence DESC
       LIMIT 5) AS rec
   FROM
     `bqml_tutorial.recommend_content`
   GROUP BY
     visitorId;

   The results should look similar to the following:

   +---------------------+-----------------+---------------------------------+
   | visitorId           | rec:contentId   | rec:predicted_rating_confidence |
   +---------------------+-----------------+-------------------------  ------+
   | 867526255058981688  | 299804319       | 0.88170525357178664             |
   |                     | 299935287       | 0.54699439944935124             |
   |                     | 299410466       | 0.53424780863188659             |
   |                     | 299826767       | 0.46949603950374219             |
   |                     | 299809748       | 0.3379991197434149              |
   +---------------------+-----------------+---------------------------------+
   | 2434264018925667659 | 299824032       | 1.3903516407308065              |
   |                     | 299410466       | 0.9921995618196483              |
   |                     | 299903877       | 0.92333625294129218             |
   |                     | 299816215       | 0.91856701667757279             |
   |                     | 299852437       | 0.86973661454890561             |
   +---------------------+-----------------+---------------------------------+
   | ...                 | ...             | ...                             |
   +---------------------+-----------------+---------------------------------+
   

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.

• You can delete the project you created.
• Or you can keep the project and delete the dataset.

Delete your dataset

Deleting your project removes all datasets and all tables in the project. If you prefer to reuse the project, you can delete the dataset you created in this tutorial:

1. If necessary, open the BigQuery page in the Google Cloud console.

   Go to the BigQuery page

2. In the navigation, click the bqml_tutorial dataset you created.

3. Click Delete dataset on the right side of the window. This action deletes the dataset, the table, and all the data.

4. In the Delete dataset dialog, confirm the delete command by typing the name of your dataset (bqml_tutorial) and then click Delete.

Delete your project

To 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.

What's next

• Try creating a matrix factorization model based on explicit feedback.
• For an overview of BigQuery ML, see Introduction to BigQuery ML.
• To learn more about machine learning, see the Machine learning crash course.