Create and manage a Content Recommendation (CR) model

This is for:

Developer System Administrator

Coveo Machine Learning (Coveo ML) Content Recommendation (CR) models suggest to the current user items that are relevant to their current browsing session. The results that are returned by the CR service can be included in a search page or in any other web page, such as in a side panel window.

To take advantage of Coveo ML CR, members with the required privileges must first create their CR models.

Note

In a Coveo In-Product Experience (IPX) interface, content recommendations are provided by IPX Recommendation (IPXRECS) models instead of Content Recommendation (CR) models. For more information, see About IPX recommendations.

Prerequisites

Ensure that view events and search action events are being pushed to Coveo Analytics for your organization (see Deploy Content Recommendations (CR)).

Even if the model only receives search or click events, it will still provide recommendations. However, we strongly recommend that you send view events on the web pages which correspond to the indexed items that you want to recommend.

Note

When there isn’t enough data for the model to recommend content, the CR model won’t return results, and the Recommendation Component will be empty.

If you recently started collecting Coveo Analytics data, recommendations will improve as more data becomes available each time the model is retrained.

Create a CR model

  1. On the Models (platform-ca | platform-eu | platform-au) page of the Coveo Administration Console, click Add model, and then click the Content Recommendations card.

  2. Click Next.

  3. (Optional) In the Learning interval section, you can change the default and recommended values for both Data period and Building frequency.

  4. Click Next.

  5. (Optional) In the Apply filters on dataset section, you can add filters to refine the data that the model uses to make its recommendations. By narrowing down the dataset, you can better customize relevancy for specific user groups and use cases. You can apply filters on all events, or on every event that belongs to a specific category, such as search, click, view, or custom events.

    Example

    You want your CR model to return recommendations that pertain to a specific user group, so you add a data filter to ensure that only a specific set of analytics are used by the model for training purposes.

    1. In the Select a dimension dropdown menu, select the dimension on which you want to base the learning of the model.

    2. In the Select an operator dropdown menu, select the appropriate operator.

    3. In the Select value(s) dropdown menu, add, type, or select the appropriate value.

    4. You can optionally add other filters by clicking Add.

  6. Click Next.

  7. (Optional) In the Recommended item types section, choose the type of items that the model will recommend.

  8. Click Next.

  9. In the Name your model input, enter a meaningful display name for the model.

  10. (Optional) Use the Project selector to associate your model with one or more projects.

  11. Click Start building.

    Note

    On the Models (platform-ca | platform-eu | platform-au) page, under the Status column, in the model row, the value is most probably Inactive.

    The model value will change to Active when the model creation is complete (typically within 30 minutes, depending on the amount of usage analytics data to process). The model can only return recommendations when its status is Active.

    For more information on Coveo ML model statuses, see the Status column reference.

  12. (Optional) Configure advanced settings for your model.

  13. Associate the model with a pipeline to use the model in a search interface and test your model to make sure it behaves as expected.

    Important

    Make sure to follow the model association leading practices before associating your model with your production query pipeline.

Advanced configuration

You can configure advanced settings for your model to suit specific use cases.

Some advanced settings are available from the model options in the Administration Console, while others are only available through JSON configuration parameters.

Notes

To specify advanced settings

  1. On the Models (platform-ca | platform-eu | platform-au) page, click the model for which you want to specify advanced settings.

  2. Do one of the following:

  1. Click Save.

Manage a CR model

You can edit using the model options or JSON, delete, or review information for your model.

Edit a CR model

  1. On the Models (platform-ca | platform-eu | platform-au) page, click the model you want to edit, and then click Edit in the Action bar.

  2. On the subpage that opens, select the Configuration tab.

  3. Under Name, you can optionally edit the model’s display name.

  4. (Optional) Use the Project selector to associate your model with one or more projects.

  5. (Optional) Under Learning interval, you can change the default and recommended Data period and Building frequency.

  6. (Optional) In the Apply filters on dataset section, you can add filters to refine the data that the model uses to make its recommendations. By narrowing down the dataset, you can better customize relevancy for specific user groups and use cases. You can apply filters on all events, or on every event that belongs to a specific category, such as search, click, view, or custom events.

    Example

    You want your CR model to return recommendations that pertain to a specific user group, so you add a data filter to ensure that only a specific set of analytics are used by the model for training purposes.

    1. In the Select a dimension dropdown menu, select the dimension on which you want to base the learning of the model.

    2. In the Select an operator dropdown menu, select the appropriate operator.

    3. In the Select value(s) dropdown menu, add, type, or select the appropriate value.

    4. You can optionally add other filters by clicking Add.

  7. (Optional) In the Recommended item types section, choose the type of items that the model will recommend.

  8. (Optional) Configure advanced settings for your model.

  9. Click Save.

    Note

    Some configuration changes initiate an automatic model rebuild when you save the model. The Models (platform-ca | platform-eu | platform-au) page shows your model’s current Status. Model settings take effect only when its status is Active.

    For more information on Coveo ML model statuses, see the Status column reference.

  10. Associate the model with a pipeline to use the model in a search interface and test your model to make sure it behaves as expected.

    Important

    Make sure to follow the model association leading practices before associating your model with your production query pipeline.

Edit a CR model JSON configuration

  1. Access the Models (platform-ca | platform-eu | platform-au) page.

  2. Click the desired model, and then click More > Edit JSON in the Action bar.

  3. In the Edit a Model JSON Configuration panel that appears, modify the existing model configuration:

    {
  "modelDisplayName": "<MODEL_DISPLAY_NAME>",
  "exportPeriod": "<EXPORT_PERIOD>",
  "intervalTime": <INTERVAL_TIME>,
  "intervalUnit": "<INTERVAL_UNIT>",
  "commonFilter": "<COMMON_FILTER>",
  "customEventFilter": "<CUSTOM_EVENT_FILTER>",
  "exportOffset": "<EXPORT_OFF_SET>",
  "searchEventFilter": "<SEARCH_EVENT_FILTER>",
  "viewEventFilter": "<VIEW_EVENT_FILTER>",
  "extraConfig": [<ADVANCED_ML_PARAMETERS>],
}

    Where:

    • modelDisplayName (string) is the name of the model appearing on the Models (platform-ca | platform-eu | platform-au) page.

    • exportPeriod (ISO-8601 string, required) is the period defining the age of the Coveo Analytics data used to build the model. Must be in the ISO8601 period format (that is, PyYmMwWdDThHmMsS).

      Note

      Unless an exportOffset is specified, the exportPeriod uses the moment when the model was generated as a base.

    • intervalTime (integer, required) is the number of intervalUnit (that is, DAY, WEEK, or MONTH) between each update of the model. Must be between 1 and 30 inclusively.

    • intervalUnit (string enum, required) is the duration unit of the interval between each update of the model. See intervalTime. Accepted values are: DAY, WEEK, and MONTH.

    • commonFilter (string) is the filter to apply to the common event dimensions (shared by all event types) in the export. Multiple filter parameters are joined with the AND operator.

    • customEventFilter (string) is the filter to apply to the custom event dimensions in the export. Multiple filter parameters are joined with the AND operator.

    • exportOffset (ISO-8601 string) is the offset of the usage analytics data used to build the model. Must be in the ISO8601 period format (that is, PyYmMwWdDThHmMsS). The default value is PT0S, meaning that all events are considered when building a model (the exportPeriod is based on the moment the model was generated).

      Example

      You want to ignore events that occur on the current day, so you set the exportOffset value to P1D.

    • searchEventFilter (string) is the filter to apply to the click and search event dimensions in the export. Multiple filter parameters are joined with the AND operator.

    • viewEventFilter (string) is the filter to apply to the view event dimensions (shared by all event types) in the export. Multiple filter parameters are joined with the AND operator.

    • extraConfig (array of string) are additional advanced parameters used to tailor the model to your use case.

  4. Click Save to apply your changes.

Delete a CR model

Note

If the model is associated with a query pipeline, make sure to dissociate the model from the query pipeline after deleting it.

  1. On the Models (platform-ca | platform-eu | platform-au) page, click the ML model that you want to delete, and then click More > Delete in the Action bar.

  2. In the panel that appears, click Delete.

Review model information

On the Models (platform-ca | platform-eu | platform-au) page, click the desired model, and then click View in the Action bar. For more information, see Reviewing model information.

Reference

Advanced model options

Test configuration mode

Note

The Test configuration mode advanced option is available only for sandbox organizations.

Sandbox organizations typically lack the amount of usage analytics data that’s required to train a model. The Test configuration mode option lets you build a model in a sandbox organization with little or infrequent usage analytics data so you can test the model.

When activated, this option reduces the amount of analytics data that’s required to build the model. It also reduces other frequency thresholds that discard queries or clicks that weren’t performed frequently enough.

Note

The usage of certain frequency thresholds, or the selection of a specific value for these frequency thresholds depends on the configuration and implementation of the model. As the possible combinations of threshold configurations are adapted for each model, these frequency thresholds aren’t listed in this section.

To activate the test configuration mode

  1. On the Models (platform-ca | platform-eu | platform-au) page, click the model for which you want to activate the test configuration mode, and then click Edit in the Action bar.

  2. On the subpage that opens, select the Advanced tab.

  3. In the left menu of the Advanced tab, select Test configuration mode.

  4. Select the Activate test configuration mode checkbox.

  5. Click Save.

URL format

You can use the URL format advanced model parameter to specify sets of patterns to find and reformat in URLs of recommended items.

Example

You want your CR model to remove the fragment in URLs, which is the part that comes after the # symbol. For example, you want https://site.org/path#section to become https://site.org/path.

Therefore, you configure the URL format advanced parameter as follows:

URL format

To configure the URL format advanced parameter

  1. On the Models (platform-ca | platform-eu | platform-au) page, click the CR model for which you want to configure the URL format advanced parameter, and then click Edit in the Action bar.

  2. On the subpage that opens, select the Advanced tab.

  3. In the left menu of the Advanced tab, select URL format.

  4. Click Add item.

  5. In the Pattern to find in URLs input, enter a regular expression that matches the pattern to find in URLs of recommended items.

  6. In the Replace by input, enter a replacement pattern to apply when a URL matches the regular expression entered in the Pattern to find in URLs input.

  7. Click Save.

Advanced JSON model parameters

userContextFields (list of strings)

The usage analytics dimensions whose values should be used as the user context by the model to influence the ranking scores of items.

Important

When configuring the userContextFields advanced parameter, make sure that the related dimension values are sent at query time in the context parameter.

To set the parameter

Access the model’s JSON editor, and then add the parameter configuration to the extraConfig object.

Example

You want to build an ML model that uses the originLevel3 and userGroups usage analytics dimensions as the user context to influence the ranking scores of items.

Therefore, you enter the following configuration in the extraConfig object:

"extraConfig": {
    "userContextFields": [
      "originLevel3",
      "userGroups"
    ]
}

automaticContextDiscovery (boolean)

Sets whether the model evaluates custom usage analytics dimensions prefixed with context_ to provide predictions or recommendations.

Default: true

When set to false, the model doesn’t automatically consider user context found in data. However, it will use user context fields defined in the userContextFields parameter.

To set the parameter

Access the model’s JSON editor, and then add the parameter configuration to the extraConfig object.

Example

You want to build a model that doesn’t evaluate custom usage analytics dimensions prefixed with context_. Therefore, you enter the following configuration in the extraConfig object:

"extraConfig": {
    "automaticContextDiscovery": false
}

urlReplacePatterns (list of tuples)

A set of patterns to find and reformat in URLs.

The first value of each tuple (that is, pattern) must be a regular expression to test against each URL.

The second value of each tuple (that is, replace) is the replacement pattern to apply when a URL matching the pattern is found.

To set the parameter

Access the model’s JSON editor, and then add the parameter configuration to the extraConfig object.

Example

You want your CR model to remove the fragment in URLs, which is the part that comes after the # symbol. For example, you want https://site.org/path#section to become https://site.org/path.

Therefore, you enter the following configuration in the extraConfig object:

"extraConfig": {
  "urlReplacePatterns": [
    {
      "pattern": "(https?:\\/\\/\\S+?)(?:#.*)?",
      "replace": "\\1"
    }
  ]
}

"Status" column

On the Models (platform-ca | platform-eu | platform-au) page of the Administration Console, the Status column indicates the current state of your Coveo ML models.

The following table lists the possible model statuses and their definitions:

Status Definition Status icon

Active

The model is active and available.

Active

Build in progress

The model is currently building.

Building

Inactive

The model isn’t ready to be queried, such as when a model was recently created or the organization is offline.
Click See more details for additional information (see Review model information).

Inactive

Limited

Build issues exist that may affect model performance.
Click See more details for additional information (see Review model information).

Limited

Soon to be archived

The model will soon be archived because it hasn’t been queried for an extended period of time.
Click Delete to remove the model.
Learn more about archived models.

Archive pending

Error

An error prevented the model from being built successfully.
If it’s a temporary system error, check back soon. Otherwise, click See more details for additional information (see Review model information).

Error

Archived

The model was archived because it hasn’t been queried for an extended period of time.
Click Delete to remove the model.
Learn more about archived models.

Archived

"Learning interval" section

In this section, you can modify the following:

  • Data period: The usage analytics data time interval on which the model will be based.

  • Building frequency: The rate at which the model is retrained.

Set the Coveo ML model training Building frequency based on the Data Period value. Less frequent for a larger Data Period and more frequent for a smaller Data Period as recommended in the following table.

Data period

Building frequency

Daily

Weekly

Monthly

1 month

check

check

3 months (Recommended)

check

6 months

check

The more data the model has access to and learns from, the better the recommendations. As a general guide, a usage analytics dataset of 10,000 queries or more typically allows a Coveo ML model to provide very relevant recommendations. You can look at your Coveo Analytics data to evaluate the volume of queries on your search hub, and ensure that your Coveo ML models are configured with a training Data period that corresponds to at least 10,000 queries. When your search hub serves a very high volume of queries, you can consider reducing the data period so that the model learns only more recent user behavior and be more responsive to trends.

A Coveo ML model regularly retrains on a more recent Coveo UA dataset, as determined by the Building frequency and Data period settings, to ensure that the model remains up-to-date with the most recent user behavior.

Note

If you’re testing the model in a sandbox environment in which very little analytics data is available to train the model, you can activate the Test configuration mode advanced option to ensure the model provides recommendations.

In the Recommended Item Types section, you can choose the types of items that a CR model recommends. By default, All types is selected, meaning that all types of content are returned. Alternatively, you can choose specific item types to filter out.

To filter out specific item types

  1. Select Specific item types.

  2. In the Select value(s) field, for each type that you want to add, type the desired content type and then click Add.

    Example

    The view events that are pushed to Coveo Analytics for your organization include various types of content, such as Article, Application, Download, and Course. You only want to use this model to recommend relevant downloads, so you add Download.

    Notes

    • The view events that are pushed to Coveo Analytics for your organization must contain appropriate contentType values.

    • The View Content Type option doesn’t behave like the other filters. All view event content types and search events are analyzed to create the model, but only pages of the specified content types are recommended.

    • Only create more than one recommendation model if you want to include recommendation windows for different content types (see the note in Deploy Content Recommendations (CR)).

Required privileges

The following table indicates the privileges required to use elements of the Models page and associated panels (see Manage privileges and Privilege reference).

Action Service - Domain Required access level

View models

Machine Learning - Models
Organization - Organization
Search - Query pipelines

View

Edit models

Organization - Organization
Search - Query pipelines

View

Machine Learning - Models

Edit