FAQ

A typical Natural Language Processing (NLP) application extracts terms based on linguistic rules and word frequency.

In addition to traditional NLP methods, Coveo Machine Learning (Coveo ML) leverages vocabulary previously employed by end users to search for content, therefore providing a more accurate picture of what’s contextually relevant to the current end user.

A typical basic query expression (q) (that is, what the end user types in a search box) contains an average of four keywords. However, some use cases require much larger chunks of text to be used as input for queries, such as an entire support case description. The default query processing algorithm isn’t designed to deal with that many keywords. Therefore, Coveo ML provides an Intelligent Term Detection (ITD) algorithm to extract only the most relevant keywords from these large strings of text. ITD extracts these keywords from both the q and {large-query-expressions} (lq), and then injects them into the lq.

To do so, ITD uses a set of usage analytics events (search and click) recorded for a search interface:

Specifically, Coveo ML offers the following model types for Coveo for Salesforce - Experience Cloud Edition:

Yes, Coveo for Sitecore supports Coveo ML when using a Cloud edition. It’s however not available in on-premises installations.

Depending on the model type, the default (recommended) model data period can be either 3 months or 6 months.

These default data periods are optimal for most implementations, typically taking into account a large dataset that covers user behavior trends.

Consider increasing the data period to get better trained recommendations when your search hubs serve less than about 10,000 user queries per month.

Consider reducing the data period when your search hubs serve significantly more than 10,000 queries per month and you want to get fresher or more trending recommendations.

For more information on model training sets, see Training and retraining.

The recommended training frequencies for each Data Period value shown in the following table provide optimal results for most implementations. When training a model using a longer Data Period, you can’t retrain the model as frequently.

Consider increasing the learning interval/training frequency when your search hubs serve at least 10,000 user queries per data period and, for example, your relevant content or your user behavior patterns are changing more frequently and you want recommendations to adapt more rapidly.

Consider reducing the learning interval/training frequency when your relevant content or your user behavior patterns are stable over time.

For more information on model training sets, see Training and retraining.

The Coveo ML service automatically manages the exact date and time at which models are retrained according to the Learning Interval/Training Frequency set for the model. You can’t set a precise retrain schedule date and time and can’t find out when the model was last retrained or the next time it will be retrained.

For more information on model training sets, see Training and retraining.

A Coveo organization administrator can enable and configure Coveo ML models in minutes via the Administration Console (see Manage models). Once configured, the machine learning model will begin building itself automatically.

Yes. You can test Coveo ML models before applying them to your query pipeline. By creating an A/B test, you can compare the performance of your current query pipeline with the performance of a new query pipeline that includes the model of your choice.

You can then review the model’s impact on the A/B Test tab of the query pipeline configuration page. Alternatively, you can use the built-in dashboard report and then evaluate the impact by comparing key metrics for both versions of the pipeline (see How Do You Measure Coveo ML Impact?).

You can also compare ART and QS models together and compare an ART model with the default index ranking (see Test models).

Coveo ML supports many languages.

As long as the events are used in the model creation, a model contains a submodel for each language that was used by the users performing those events (see Language). Therefore, Coveo ML models support many languages simultaneously and multilingual search (see Coveo Machine Learning models).

Yes. Coveo ML works on communities where users must log in as long as most authenticated users have access to a significant shared body of content so Coveo ML can learn from the crowd.

Most Coveo ML model types learn from user interactions on your website. The more events a Coveo ML model has to learn from, the better it will be at providing relevant results. If well implemented, Coveo ML generally reaches its best optimization learning from 25,000 to 100,000 queries (see Coveo implementation guide).

The time to improve relevance therefore depends on the level of search activity on your community and when you started gathering usage analytics data.

You can use the following 2 traditional marketing metrics to evaluate how successfully your community search connects users with the information they need to solve their specific issue:

Coveo ML optimizes search results and query suggestions, and will therefore improve CTR and ACR metrics and contribute to increase self-service. You can test the addition of a Coveo ML model like any other query pipeline changes (see Manage A/B tests or Test models).

Coveo ML uses machine learning techniques to analyze the search activity data captured by Coveo Usage Analytics (Coveo UA). Coveo ART tracks what users search for, if and how they reformulate their queries, what results they click, and whether they created a new support case. With this data, ART trains an algorithmic model for predicting which content will be most helpful to future users based upon their specific query. ART regularly retrains its model so that over time it gets better and better and adapts to new trends (see About Automatic Relevance Tuning).

ART evaluates user visits as a whole and allocates additional weight (that is, gives a higher score) to the last clicked item rather than the previously clicked ones.

Coveo ML doesn’t recommend search results or query suggestions until sufficient usage analytics data is available. Coveo ML models will start to make recommendations when it’s retrained and will get better and better each time it’s retrained with more data.

No. Coveo ML doesn’t work with a full on-premises Coveo deployment (on-premises CES index, usage analytics module, REST Search API). Coveo ML requires Coveo REST Search API and usage analytics.