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Managing Coveo Machine Learning Automatic Relevance Tuning in a Query Pipeline

Coveo™ Machine Learning (Coveo ML) is a service that leverages usage analytics data (see Coveo Machine Learning). Automatic relevance tuning (ART), is a Coveo ML feature that optimizes search results relevance based on user search behavior (see Automatic Relevance Tuning (ART) Feature).

In short, ART looks at end-user query and search result click behavior made over a given period of time to learn what the best search results for each query are. When a learned query is performed again, ART recommends the best learned search results by boosting their ranking score so they appear among the top 10 results.

Notes:

  • ART recommends the 5 best learned search results, but may recommend less than 5 when:

    • A query is not very frequent and less than 5 items were learned for that query.

    • Some of the recommendations are secured and not accessible to the user performing the query.

    • Some of the recommendations are filtered out because they do not match filters such as the current facet selections.

  • On an empty query, ART returns the most clicked documents during the data period of the model (see Data Period).

ART can inject items that would not be normally included in search results because they where learned to be relevant even if they do not contain some or all of the searched keywords. This is one of the key ART benefits as a user can find the most useful items without having to type the right keywords or the specific synonym contained these items. This is the default behavior (Match the query parameter is false).

Note: ART currently ignores all special characters or operators in the user entered query to only keep the keywords and therefore ignores the special behavior described in Using Special Characters in Queries or Search Prefixes and Operators.

This ART behavior may lead to unexpected search results when users want to take advantage of more advanced query syntax.

Example: A user is searching for items containing a specific phrase by entering the phrase enclosed in double-quotes (see Searching a Phrase) and no items contain the phrase. The user would expect to get no search results, but ART can inject items that were clicked for similar queries made of one or some of the searched keywords (not in a phrase search).

You configure ART from the administration console for a query pipeline defined in your Coveo Organization by adding a machine learning model (see Coveo Machine Learning Models and Who Can Perform the Page Actions). The default values for available parameters are typically appropriate in most cases, but the following procedure indicates why or when you may want to change them.

What's Next?

If the query pipeline in which you configure ART contains thesaurus rules, help train ART to learn those rules (see Helping Train Coveo Machine Learning With Thesaurus Rules).

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