- Understanding Custom Context
- Leveraging Custom Contexts
- Feature Selection
- Event Recommendations Deployment Overview
- Deploying Dynamic Navigation Experience
- Helping Train ART Models by Linking Queries to Results
- Adding Coveo Machine Learning Blacklist Words
- About the PermanentId Field
Coveo Machine Learning Features
The Coveo™ Machine Learning (Coveo ML) service currently offers the features described in the following sections (see Coveo Machine Learning).
By default, models are built for each combination of language, hubs, and tabs since these attributes normally define different type of users and use cases. Thus, out of the box, Coveo ML models do not deliver recommendations or suggestions based on user behavior in another search hub, search interface, or language.
Query suggestions that were recommended based on your internal search interface logged events are not recommended in your external search interface.
When you want the relevance on one search page to influence other search interfaces for a unified experience, you can set the filterFields custom model parameter value accordingly. If the parameter value only contains the desired search page, the model will provide recommendations or suggestions based on user behavior on that specific search page even if the model is active on a search interface in another hub. Before modifying the value, it is strongly recommended to consult your Coveo Customer Success Manager (CSM) or Coveo Support for appropriate guidance. Moreover, you should test changes thoroughly in a sandbox environment before deploying in production (see About Non-Production Coveo Cloud Organizations).
For further information on Coveo ML, see the Coveo Machine Learning FAQ section.
In short, the ART feature learns what search users seek and delivers it.
In more detail, ART analyzes user behavior patterns from many usage analytics search visit actions (such as query reformulation, clicked results, if a support case was submitted) to understand which clicked results and content lead to successful outcomes such as self-service success, and automatically adjusts future search results so that the best performing content always rises to the top.
ART excels with popular and ambiguous queries where users enter only one or two terms, as well as with paragraph-sized queries expressing long descriptions. ART is robust to common typographical errors and learns implicit synonyms. When your Coveo index content includes secured items, ART queries the index to ensure to only recommend items 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. Members of the Administrators and Relevance Managers built-in groups can configure and activate ART in just a few clicks (see Adding and Managing Coveo Machine Learning Models).
By default, ART model recommendations are based on the language of the user’s query as well as the search hub and search tab (interface) in which the query was performed. One model is made per search hub/search tab/language combination.
Items are boosted only if they were clicked in the same language, hub, and interface as the current query.
The Coveo ML Query Suggestions feature recommends significantly more relevant queries to users as they type in the search box.
The Coveo ML Query Suggestions feature:
Identifies typed characters exact, partial, or fuzzy matches anywhere in any individual keyword appearing in any order.
Stems query suggestion keywords to remove duplicates.
Offers the most relevant recommendations by ranking query suggestions considering:
Only considers queries that were performed and followed by clicks on search results enough times in order to prevent infrequent queries to pollute the suggestions (see Reviewing Coveo Machine Learning Query Suggestion Candidates).
Members of the Administrators and Relevance Managers built-in groups can configure and activate Coveo ML Query Suggestions in a few clicks (see Adding and Managing Coveo Machine Learning Models). Developers can leverage the feature in the desired search interface (see Providing Coveo Machine Learning Query Suggestions).
The Coveo ML Event Recommendations feature learns from your website user page and search navigation history to return the most likely relevant content for each user in his current session. The Recommendation service results can be included in a search page or in any web page such as in a side panel window (see Coveo Machine Learning Event Recommendations Deployment Overview).
The recommendations can be interpreted as “
People who viewed this page also viewed the following pages”.
Your company offers product technical documentation and Q&A content on several public websites for customer end users, administrators, and developers. These websites are configured to send all views to the Coveo Usage Analytics service. Your website pages include Recommended Articles side panel windows.
The recommendation algorithm is based on the co-occurrence of events such as view events within a user session. When two events abnormally frequently co-occur within sessions, the algorithm learns that they are linked. When one event is seen, the model recommends the other.
Event Recommendations model suggestions are provided based only on the user language, since view events are not logged from a search hub.
The Coveo ML DNE feature learns from usage analytics events to pertinently order facets and facet values according to user queries. More precisely, DNE models analyze queries and target specific user behaviors such as result clicks and facet selections to make the most relevant facets appear at the top for a given query.
Furthermore, 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.
Coveo ML DNE feature also 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.
- Screen size
- Storage capacity
You enable a Coveo ML DNE model. When your search interface sends a query to the Search API in order to request facets, the DNE model can now modify that query in the query pipeline. It applies insights gained from the analysis of past customer behavior and determines that users are most likely to sort search results using the Brand and the Price facets. Your search page now displays facets in the following order:
- Screen size
- Storage capacity
Before you enabled Coveo ML DNE, the Brand facet displayed its facet values in the following order when customers searched for
Since the Coveo ML DNE model determined that customers are more likely to shop for Apple smartphones, the model modifies the user query to boost Apple smartphone result list items.