Create and Manage a Product Recommendations (PR) Model

Coveo Machine Learning (Coveo ML) Product Recommendations (PR) models use usage analytics (UA) events to relevantly target and suggest products to your customers. Therefore, you must follow the Commerce Data Health Implementation Guide to ensure that your commerce interfaces track commerce usage analytics events.

Depending on the recommendation strategy you want to leverage, ensure your commerce interface is tracking the proper events:

On the Models (platform-eu | platform-au) page, in the upper-right corner, click Add Model to open the Add a Machine Learning Model panel.

Under Name, enter a meaningful display name for the model.

Under Model type, select Product Recommendations, and then click Next.

(Optional) In the Learning Interval section, change the default and recommended Frequency and Data period values, and then click Next.

(Optional) In the Learn From section, add filters to refine the data that the model uses to make its recommendations.

Click Add Model.

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

Note 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.

(Optional) If you want to specify advanced parameters for your model, you can use the Advanced tab of the model configuration.

Once the model is Active, you must associate the model with a pipeline to take advantage of the model in a search interface. The model can be re-used in many query pipeline associations.

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

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

On the subpage that opens, select the Configuration tab.

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

(Optional) In the Learning Interval section, change the Frequency and Data period values.

(Optional) In the Learn From section, add filters to refine the data that the model uses to make its recommendations.

Click Save.

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

Note The model value will change to Active when the model edition 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.

Once the model is Active, you must associate the model with a pipeline to take advantage of the model in a search interface. The model can be re-used in many query pipeline associations.

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

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

On the right side of the page, click , and then select Add model with JSON.

In the Add a Model With JSON panel that appears, select Model configuration, and then modify the model placeholder configuration:

{
    "engineId": "ecommerce",
    "modelName": "<MODEL_NAME>",
    "modelDisplayName": "<MODEL_DISPLAY_NAME>",
    "exportPeriod": "<EXPORT_PERIOD>",
    "intervalTime": <INTERVAL_TIME>,
    "intervalUnit": "<INTERVAL_UNIT>"
}

Click Add Model.

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

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

In the Edit a Model JSON Configuration panel that appears, select Model configuration, and then modify the existing model configuration:

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

Click Save to apply your changes.

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

In the Delete a Model panel that appears, click Delete model.