Leverage Machine Learning
- Machine Learning Overview
- About Product Recommendations (PR)
- About Dynamic Navigation Experience (DNE)
- About Automatic Relevance Tuning (ART)
- About Query Suggestions (QS)
- About Content Recommendations (CR)
- About Smart Snippets
- About Coveo Machine Learning Case Classification (CC)
- Manage Models
- Custom Context
- User Profile
- About User Stitching
Coveo Machine Learning FAQ
Coveo Machine Learning (Coveo ML) is a service that leverages usage analytics data to deliver relevant search results and proactive recommendations (see Coveo Machine Learning). Coveo ML offers a few features meant to improve content relevancy using predictive models to make recommendations.
This article contains some questions you may ask yourself about Coveo ML, its features, and its models.
How Does Intelligent Term Detection (ITD) Work?
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) (i.e., 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 such large query expressions (
It selects up to 2,500 queries that generated a positive outcome (i.e., a query result was opened) and were performed at least five times. The selected queries are called top user queries.
If there are more than 2,500 queries that were made five or more times, then ITD selects the 2,500 most popular ones.
It establishes a correlation between the top user queries and the keywords contained in the
lq(e.g., support case description).
It finds the five most relevant terms in the
lqbased on the average importance of each term (see TFIDF), and on the longest substring in the
lqthat’s contained in the top user queries. These five terms are called the refined keywords.
It overrides the original
lqwith the refined keywords before the query is executed against the index.
How Do I Enable ITD?
Once the model is created and active, you can associate the model with the query pipeline to which your search interface traffic is directed.
When associating your ART model with a query pipeline, you must select the Comply with Intelligent Term Detection (ITD) check box.
How Can I Verify That ITD Works Properly?
You have associated the ART model with the query pipeline to which your search interface traffic is directed. In addition, the Comply with Intelligent Term Detection (ITD) option is activated for this association. To verify whether ITD works as expected:
Access the search interface you want to test (typically a case creation form).
Send a query for which the model is able to recommend items (see Review Coveo Machine Learning Model Information).
In your browser developer tools, in the Network tab, under the Name column, select the latest request to the Search API. The request path should contain
Select the Preview tab. You should now see the query response body.
In the query response body, you should see an expandable
refinedKeywordsproperty. You can expand it to see the keywords extracted from the
lqby ITD. If nothing is shown in the
refinedKeywordsproperty, it means that ITD didn’t extract keywords from the current
When the ART model with the ITD option enabled hasn’t gather enough data to provide refined keywords, the
What Exactly Are the Coveo ML Capabilities for Salesforce Communities?
Specifically, Coveo ML offers the following features for Coveo for Salesforce - Experience Cloud Edition:
Case Classification to render relevant classification suggestions in support cases.
Smart Snippets to provide answers to user queries directly on the search results page.
Does Coveo ML Support Coveo for Sitecore?
Yes, Coveo for Sitecore supports Coveo ML when using a Cloud edition. It’s however not available in on-premises installations.
What’s the Optimal Model Data Period?
Depending on the model type, the default (recommended) model data period can be either
3 months or
These default data periods are optimal for most implementations, typically taking into account a large data set 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.
When Should I Change a Model Learning Interval/Training Frequency?
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.
|3 months (Recommended)|
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.
When Exactly Is a Model Retrained?
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.
How Are Coveo ML Features Deployed?
Can Coveo ML Features Be Tested Before They Are Activated?
Yes. You can test Coveo ML features on a test query pipeline before deploying it in your production environment. You can start using the test query pipeline offline and then perform A/B tests on real queries (see Test Query Pipeline Changes).
You can also compare ML (ART and QS) models together and compare an ART model with the default index ranking (see Testing Coveo Machine Learning Models).
What Languages Do Coveo ML Features Support?
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).
Do Coveo ML Features Work on a Secured Salesforce Community?
Yes. Coveo ML features work 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.
How Long Do Coveo ML Features Take to Start Improving Relevance?
Most Coveo ML features 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 Platform Project 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.
How Do You Measure the Coveo ML Features Impact?
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:
Click-Through Rate (CTR) — The percentage of users clicking on any link on the search results page. Higher values are better.
Average Click Rank (ACR) — Similar in concept to page rank, this metric measures the average position of clicked items in a given set of search results. Lower values are better, as a value of
1represents the first result in a list.
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 Coveo ML features like any other query pipeline changes (see Test Query Pipeline Changes or Testing Coveo Machine Learning Models).
Evaluate Automatic Relevance Tuning (ART) Impact
You want to determine the percentage of users who clicked on ART-promoted results in a given month. Therefore, you create a usage analytics pie chart card using the Click Ranking Modifier dimension and the Click Event Count metric.
The newly created pie chart card now shows the number and the percentage of clicks made on results promoted by ART.
Evaluate Query Suggestions (QS) Impact
You can use usage analytics reports to obtain the number of search events originating from query suggestions by using the Search Cause dimension.
You want to know the number of search events that originated from query suggestions in a given month. Therefore, you create a usage analytics pie chart card using the Search Cause dimension and the Search Event Count metric.
The newly crated pie chart card now shows the total number of search events that occurred for each available search cause.
You can inspect the number of search events that originated from query suggestions by looking at the
How Exactly Does Automatic Relevance Tuning (ART) Work?
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 on, 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).
How Does ART Ensure That the First Results Are Not Falsely Promoted by Users Clicking Them Solely Because They Are the First Results?
ART evaluates user visits as a whole and allocates additional weight (i.e., gives a higher score) to the last clicked item rather than the previously clicked ones.
A user performs a query and inspects the returned results:
The user clicks the top two items, but these don’t answer their inquiry.
The user clicks the three following results, and then closes the search page.
ART assumes the fifth result is the best result (i.e., answer the user inquiry) since the click on this result is the last user action associated with the search event.
As the ART model is rebuilt over time, the fifth result climbs the result list based on the additional weight acquired from being the last clicked item.
What Happens When Enabling Usage Analytics and Coveo ML Features at the Same Time?
Coveo ML doesn’t recommend search results or query suggestions until sufficient usage analytics data is available. Coveo ML features will start to make recommendations when it’s retrained and will get better and better each time it’s retrained with more data.
Can Coveo ML Features Work with Coveo On-Premises?
No. Coveo ML features don’t work with a full on-premises Coveo deployment (on-premises CES index, usage analytics module, REST Search API). Coveo ML features require Coveo Cloud REST Search API and usage analytics.