Create an Automatic Relevance Tuning Model


Ensure that your Coveo organization has collected enough usage analytics data before creating an ART model.

When your search interface has more than 55 visits per day in which a user query is followed by a click for a specific language, wait until the minimum data requirement is met, and then create a model with the proper training set.

Creating an ART Model

Advanced users may want to create a Coveo ML model using JSON.

  1. On the Models page, in the upper-right corner, click Add Model to open the Add a Machine Learning Model panel.

  2. In the Add a Machine Learning Model panel, under Name enter a meaningful display name for the model.

  3. Under Model type, select Automatic Relevance Tuning, and then click Next.

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

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

  6. Click Add Model.

  7. On the Models page, under the Status column, in the model row, the value is most probably Inactive.

    This value will change to Active when the model creation or modification 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.

  8. Once active, you can test your model to ensure that it behaves as expected.

  9. Once you’re satisfied with the test results, you must associate the model with a pipeline to take advantage of the model in a search interface.

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