About Automatic Relevance Tuning (ART)

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In short, Automatic Relevance Tuning (ART) models learn what search users seek and delivers it.

In more detail, ART analyzes user behavior patterns from many Coveo Usage Analytics (Coveo UA) search visit actions (for example, query reformulation, clicked results^[1], whether a support case was submitted) to understand which clicked results and content lead to successful outcomes, such as self-service success. It automatically adjusts future search results, so that the best-performing content always rises to the top.

Result page showing ART-promoted results | Coveo

ART excels with popular and ambiguous queries, in which users only enter one or two terms, as well as with paragraph-sized queries expressing long descriptions. ART can handle common typographical errors, and it learns implicit synonyms. When your Coveo index includes content from sources that index permissions, ART queries the index to ensure that it only recommends items that the user performing the query is allowed to access.

In practice, ART boosts the ranking weight of recommended items so that they appear among the top search results. ART can also take advantage of the atomic-result-badge component to highlight items that have been promoted in the results list.

Members with the required privileges can create, manage, and activate an ART model in just a few clicks.

Notes

By default, ART model recommendations are based on the language of the user’s query as well as the search interface in which the query is performed. ART models build a submodel for each language, and then apply filters on these submodels for each search hub and search tab to better tailor the provided recommendations to the user’s context.
Items are boosted only if they were clicked in the same search interface as the current query.
You can change this default behavior by modifying the default model’s suggestion filters.

Leading practice

You can use the Relevance Inspector to identify search results promoted by ART.

1. ART models also learn from actions performed by users within a given search result (for example, clicking a search result Quick view or attaching a result to a Case in a Coveo Insight Panel).