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Coveo Machine Learning Features

The Coveo™ Machine Learning (Coveo ML) service (see Coveo Machine Learning) currently offers the features described in the following sections.

Note: Models are built for each combination of language, hubs, and tabs since these attributes normally define different type of users and use cases. Thus, Coveo Machine Learning models do not deliver recommendations or suggestions based on user behavior in another search hub, search interface, or language.

Example: Query suggestions that were recommended based on your internal search interface logged events are not recommended in your external search interface.

Automatic Relevance Tuning (ART) Feature

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. 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. A Coveo Cloud organization administrator can configure and activate ART in just a few clicks (see Managing Coveo Machine Learning Automatic Relevance Tuning in a Query Pipeline).

Note: 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.

Query Suggestions Feature

The Coveo ML Query Suggestions feature recommends significantly more relevant queries to users as they type in the search box. The original usage analytics query suggestions are limited to top queries in which the typed characters exactly match a suggested query part.

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:

    • The number of times the query was performed.

    • The degree of matching.

    • The query performance based on the Relevance Index and Click-Through usage analytics metrics.

  • Only considers queries performed at least 10 times and for which at least 5 had a search result clicked to eliminate outliers.

In the end, suggested queries are surprisingly tolerant to typos, and get better as your usage analytics data set size increases.

A Coveo Cloud organization administrator can configure and activate Coveo ML Query Suggestions in a few clicks (see Managing Machine Learning Query Suggestions in a Query Pipeline).

The JavaScript Search Omnibox can easily be configured to provide Coveo ML query suggestions (see Omnibox Component - enableRevealQuerySuggestAddon). By default, the Omnibox highlights exact matches in bold, fuzzy matches in bold and italic, while a keyword completion in the search box appears in gray.

Note: By default, Query Suggestions model recommendations are made based on the language of the user's query, the search hub or the search tab (interface) in which the query is performed. Query Suggestions are only returned if the language, search hub, or search interface matches the current user context.

Recommendations Feature

The Coveo ML Recommendation 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 Recommendations Deployment Overview).

The recommendations can be interpreted as "People who viewed this page also viewed the following pages".

Example: 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 page 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 the events such as page views 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.

Note: Recommendations model suggestions are provided based only on the user's language since page views are not done in a search hub or a search interface.

Last update: June 22, 2017