About Dynamic Navigation Experience (DNE)

Ecommerce Developer Product Documentation

In this article

About the Autoselection Feature
About the Facet Generator Feature

Coveo Machine Learning (Coveo ML) Dynamic Navigation Experience (DNE) models leverage usage analytics events to pertinently order facets and facet values according to the user query and language. More precisely, DNE models analyze queries and actions performed by previous users (e.g., clicked results, facet selections) to make the most relevant facets appear at the top for a given query.

Coveo ML DNE models also reorder facet values within a given facet to make the most popular values appear at the top. To do so, the models use the search events performed by previous users who have selected certain facet values for a specific query.

Furthermore, Coveo ML DNE offers a facet value autoselection feature that improves the user experience by automatically selecting facet values according to user queries.

A Coveo ML DNE model uses its facet value ranking to boost search results. The model uses the most popular facet values for a certain query and applies query ranking expressions (QREs) to boost the search results whose field values match the values of those facets.

Members with the required privileges can create, manage, and deploy a DNE model.

About the Autoselection Feature

Since the Coveo JavaScript Search Framework January 2020 release, it’s possible to activate the DNE autoselection feature in your Coveo-powered search interfaces.^[1]

The DNE autoselection feature automatically selects facet values according to the end-user query. The feature learns from your end-users behaviors to understand which facet values are the most relevant according to their current browsing task.

Example

For a Coveo-powered clothing commerce interface, a Coveo administrator created a DNE model and chose to enable the autoselection feature for the category and gender facets when configuring the model.

When accessing the commerce interface, a customer first searches for a hat.

Based on the current context and recorded usage analytics data, the model determines that the Hats and Hats and Earmuffs values of the category facet are relevant enough to be automatically selected and refine the user query.

After looking at the search results, the user performs the skirt query.

Following the same process, the model determines that the Skirt and Dresses value of the category facet and the Women value of the Gender facet are relevant enough to be automatically selected and refine the user query.

The Facet Generator must be enabled to generate facets dynamically. Contact us to enable this feature.

The Facet Generator is a user interface helper that dynamically generates the most relevant facets based on your indexed content. To achieve this behaviour, the Coveo index assigns a score to each facetable attribute and displays the most appropriate ones.

This feature aims at reducing the manual configuration necessary for displaying relevant facets in a search interface, especially in catalogs with a large number of product attributes.

Example

When accessing a commerce interface, a customer searches for a Surf Board.

Based on the indexed items, the model displays all the relevant search facets. Of the most relevant facets displayed, Color, Fin System, and Tail Shape let the user refine their query further.