If your user role has
Personalization/Edit permission that includes your website, you can modify
model settings according to your requirements.
Here, you can click the Edit icon to access the model configuration screen and modify the settings, for example:
- A timeframe over which the algorithm gathers events that are used in the calculation
- Submodels that can narrow down the list of model results
- Segments that allow getting personalized content suitable for particular user groups
- A list of items included or excluded from the model
For more information and a list of model types, see Recommendation models.
Advanced model configuration¶
Most of the models provide additional configuration parameters, which enable customization.
The parameters supported by different model types are described in the table below. Some models support submodels. Additional differentiation criterion is the supported context. If a model requires context, it can only be linked to scenarios that provide the necessary context.
|Model type||Available parameters||Submodel support||Context|
|Popularity||Relevant event history defines the time period for which the statistics must be analyzed. Depending on the type of product, it can be between several months and several hours. Fast event ageing can be used to weight newer events higher than older events.||yessubmodels based on category are enabled by default||not needed|
|Also clicked/purchased / Ultimately bought||Both also clicked and ultimately purchased models allow defining the relevant event history.||yes, manual||required (either context items or user data)|
|Random||This model requires the maximum age for the items that should be recommended by this model.||no||not supported|
|History-based||The type of the history (CLICK-history or BUY-history) must be specified.||no||required (user data)|
|Editor-based||The list of recommendations must be created manually by the editor.||no||not supported|
|Blacklist||The list of items that should be excluded from the recommendations must be created manually by the editor.||no||not supported|
Do not confuse event history age with item age. History age is the age of the user's footprint (for example, "User clicked on the product A two weeks ago"). Item age is the time over which the item is available in the shop ("How new is the item"). The history is recorded automatically based on event tracking. The item catalog must be filled separately as a result of data import.
Segments allow getting personalized content suitable for particular user groups. They compute models based on segment attribute factor. Information with user segment is provided in each event which comes from the tracking script.
If your user Role includes
Segment group/All functions Policies, you can configure segment settings in the models according to your requirements.
To do this, go to Personalization > Models, and click the Edit icon next to a name of the model.
With segment groups you can assign users to different recommendation groups based on data gathered and deliver recommendations to these user groups.
The Segment list displays only active segments and is generated from the events collected for relevant history (the actual data from recommendation engine, not what is added using the Back Office).
The value of each segment is transfered to the event.
Models are displayed only for a selected time period.
If a group is inactive for a certain period of time, the segments get
Inactive status and cannot be used.
Trigger model build¶
Models on the Personalization server side are configured to build at intervals, for example, every 24-hours. For models which require computation (all popularity and collaborative models), you can manually trigger the build, for example, after you modify model settings.
To do this, go to Personalization > Models.
Click the edit icon next to the model name, make necessary changes, and click
the Trigger model build button.
On the list of models, the model's status changes to
Build in progress.
When the build come our successful, the status changes to
Possible model statuses:
- Active - model is successfully built
- Not active - new model which hasn’t been triggered or used yet, or model that is added to the scenario, calculated and then removed from the scenario
- Build in progress - model during the building process
- Failed - there is no data to build the model or some error occurred, building failed