The recommendation monitoring dashboard allows you to immediately see the aggregates of all important metrics for a specific recommendation engine. This article explains what the numbers your dashboard represent. If you want to access the dashboard go to `
Campaigns` > `
Recommendations` > the particular recommendation engine you want to evaluate > `
In the upper right corner, you can always specify the specific timeframe that you want to analyze.
# Engine status
When you open a recommendation engine there is always a small dot to the left of its name. This dot is a visual indicator of the recommendation's status. Different colors signify different statuses which you can see when you hover your mouse over the dot.
The list of different statuses is as follows:
|Running (green)||The engine is running and uses the newest data.||Highest quality|
|Suspended (green)||The engine is suspended because it wasn’t used in the last 14 days.||Highest quality (after the engine is resumed with first request)|
|Limited (orange)||The engine is running but is not trained on the latest data. In the case of the Advanced model, one of the underlying engines isn’t working properly or isn’t trained on the latest data.||High quality|
|Pending (yellow)||Engine is waiting to be trained. The saved engine should be available within 24h.||Random fallback or no recommendations (until the model is trained)|
|Failed (red)||The engine failed to be created or is unavailable (technical problem).||Random fallback or no recommendations|
|Inactive (gray)||Engine was disabled or deleted.||No recommendations|
|Unknown (gray)||We were unable to detect the state of the engine.||Random fallback or no recommendations (unexpected technical problem)|
|Draft (blank)||The engine was not saved yet.||No recommendations|
# Recommendations performance
The recommendations performance section contains information about the usage of the recommendation engine and its performance in terms of availability and personalization.
## Recommended items
The total number of items recommended by the recommendations engine within the specified timeframe. If a single product is recommended multiple times to a single customer it counts each time as an additional recommended item.
Percentage of all the items recommended by the recommendations engine within the specified timeframe, which were generated through personalized recommendation. This means recommendations that were made for a specific user based on their behavior on the website and their browsing history.
Percentage of all the items recommended by the recommendations engine within the specified timeframe, which were generated through non-personalized recommendations. This means recommendations that were made based on some generic metrics like 'newest' or 'most popular'.
Percentage of all the items recommended by the recommendations engine within the specified timeframe, which were generated by the fallback mechanism. This means recommendations that were randomly chosen for the recommendation but still respecting the set catalog filters. Fallback is used if the main engine is unable to deliver recommendations due to some problems (not enough data, timeout, engine unavailability). Turn it off by setting the request parameter fillWithRandom to false.
## Performance share
Visual representation of the components of recommended items showing how big a share of items recommended by the recommendation engine fell in each of the three above categories within the specified timeframe.
## Catalog usage
Percentage of all the items recommended by the recommendations engine within the specified timeframe, which are in the product catalog.
# Engine provisioning
Engine provisioning stops and resumes recommendation engines automatically based on their usage. If an engine is not used for an extended period of time, it is stopped. Once the engine is used again, it resumes automatically.
Unused engines are suspended automatically after 14 days of inactivity (i.e. no requests received in the last 14 days). After an engine is suspended, it can be easily resumed either manually by hitting the “Enable model” button in the engine configuration section or by sending a request to it (including previewing in the engine test tab or campaign preview). The resumed engine should be up and running with a freshly trained model in a matter of seconds as it is still being trained in the background during the suspension.
After 35 days of inactivity, training of the suspended engine also stops. This means that 21 days after an engine is suspended, it can take up to 24h to resume the engine as it is possible that it needs to be trained again.