Creating recommendations

Once the model training is completed, you can create a recommendation. The recommendations you create and activate will not be visible until you indicate how and where the recommendations are to be displayed.

Because each recommendation is different for every user, you can’t indicate exactly the items to be shown in the recommendations. However, you can preview the recommendation for any customer.

Distributing recommendations


You can use the ID of the recommendation and insert it in other types of communication, such as:

  • dynamic content - this way you can show the recommendations on your website.
  • email - this way you can send out recommended items through emails.
  • mobile application - you can use documents to build your own mobile app and show the recommended items.
  • mobile push - you can send recommendations through notifications in your mobile application.
  • web push - this way you can send notifications to your customers through a web browser.
  • SMS - this way you can reach your customers with recommendations on their mobile.

You may want to read


Prerequisites


  • You must configure AI engine.
  • You must be granted a set of user permissions that allow you to access the Communication module, to create, to edit and execute recommendations.
  • If you want to create the Recent interactions recommendation, create an aggregate that gathers a group of items towards which a specific event has occurred
    Click here to see example aggregate

    Type & Source section
    The Type & Source section

    Required in the configuration:

    • Aggregate type set as LAST_MULTI
    • Event
    • Event parameter connected to an item
    Note: You can read more about aggregates here.

Creating recommendation


  1. Go to Communication icon Communication > Recommendations v2 > Add recommendation.
  2. Enter the name of the recommendation (it is only visible on the list of recommendation).

Select feed and recommendation type


Start with selecting the feed from which items will be sourced to recommendations and the type of recommendations to be displayed.

Type & Source section
The Type & Source section
  1. In the Type & source section, click Define.
  2. From the Catalog dropdown list, select an item feed.
  3. Below, select the recommendation type.
    If the recommendations are greyed out, it means the AI engine is not trained yet or that a recommendation type is not available for the particular catalog.
    Note:

    You can find more information about:

  4. Confirm the settings by clicking Apply.

Select an aggregate (only recent interactions model)


Only for Recent interactions

Important: This section is available only for the Recent interactions recommendation type.
Selecting an aggregate for the recent interactions recommendation type
Selecting an aggregate for the recent interactions recommendation type
  1. Select the aggregate you created within the scope of Prerequisites
  2. Confirm the selection by clicking Apply.
  3. Proceed to Configuring comparison attributes.
Note: You can read more about aggregates here.

Configure comparison attributes (only item comparison model)


Only for Item comparison

Important: This section is available only for the Item comparison recommendation type.
A blank form for configuring recommendation campaigns
A blank form for configuring a recommendation

You can select the attributes of the items to be included in the comparison. The attributes in the Predefined attributes section are sourced from the Response attributes which can be edited in Settings > AI Engine Configuration.

  1. To add more attributes apart from the predefined ones to the comparison, in the Additional attributes, click Select attributes.
  2. On the pop-up, select the attributes.
  3. Confirm by clicking Apply.
    Result: Example item comparison on a website (the context item is included in one of the columns):
    Example item comparison frame
    Example item comparison frame

Configure item slots


You can use slots to assign space in your recommendation frame to specific items. Each slot may include a different number of items and have its own filtering rules.

A blank form for configuring recommendation campaigns
A blank form for configuring a recommendation

For example, you can use three slots to display:

  • Items of specific brand - This allows you to use space in your recommendation slots by items of your partners and suppliers.
  • Items of specific category,
  • Items of specific color

A recommendation must include at least one slot.

  1. In the Items section, click Define.
  2. If you’re adding more than one slot and want to define the order of displaying them, toggle the Keep slots in order option.
    • When the option is disabled, the slots will be arranged according to their score in the recommendation and boosting factors.
    • When the option is enabled, the numbers next to the names of the slots indicate their display order.

Adding slots

  1. Click Add slot.
  2. If you want to name the slot, hover over Unnamed slot bar and click the pencil icon which appears at the right side of the bar.
  3. Click the created slot to expand its settings.
  4. Enter the minimum and maximum number of items to include in the slot.
  5. Add the filters for this slot.
  6. Confirm the settings by clicking Apply.

Elastic filters

Apart from selecting the items to be included in the slot, the elastic filter supplements the slot if it’s not entirely filled up with the items.
For example, if you select to display up to 10 items, and you have only 5 items that meet the conditions of elastic filter to be included in the slot, then the slot will be filled with additional items which do not match the elastic filter (based on scoring).

  1. Click Define filter.
  2. Select one of the filter creators:

Static filters

When you use a static filter, it shows the fixed number of items that match the conditions of the filter. If there are not enough items to fill in the recommendation slot, the slot is not displayed at all.

  1. Click Define filter.
  2. Select one of the filter creators:

Distinct filter

Distinct filters allow you to increase the variety of items included in the slot. You can define exactly the allowed number of items that share the same attribute value to be shown, for example, a number of items that have the same brand, color, shape, category, and so on.

  • For all recommendation types except for Last seen, the engine considers up to 1000 items with the highest score that match the recommendation type. For example, if you selected the Cross-sell type, the engine analyzes up to 1000 items that match the cross-sell recommendation type, and then selects the number of items you chose to include in the slot.
  • For the Last seen recommendation type, the engine considers the last 100 page visit events. Based on the data from these events, the engine selects the number of items you chose to include in the slot.
Important: This filter can only use attributes defined when configuring the recommendation engine.
  1. Click Define filter.

    1. If you want the distinct filter to supplement the slot with non-matching items in case not enough matching items are found, enable the Mark filter as elastic option.
    2. From the list, select an attribute.
    3. In Max no. of occurrences, define the number of items with the same value of the attribute (for example, a brand) that can be included in the slot.
      Result:
    A blank form for configuring recommendation campaigns
    Attributes defined in the distinct filters

    Explanation: In the example in the screenshot, a slot displays items of various brands (a brand cannot be repeated more than once), various colors (each item in the slot is in a different color), and only 3 items in the slot can be taken from the lowest category level.

Define the boosting factors


You can influence the arrangement of items in the recommendation, which is by default arranged according to a score provided by the model (the type of recommendation selected). You can boost the items in two ways (both can be used at the same time):

  • Metric boosting uses pre-defined item popularity metrics to influence item scoring.
  • Attribute boosting influences item scoring depending on rules applied to the item’s attributes. This is done by applying a filter to items and adjusting the score of those items that match the filter. For example, you can increase the scoring of a particular brand without excluding other brands entirely.
An example of a Boosting section, with metric boosting used to promote items and attribute boosting used to demote items
An example of a Boosting section, with metric boosting used to promote items and attribute boosting used to demote items
  1. In the Boosting section, click Define.
  2. Metric boosting:
    1. Click Select and select a metric to use for adjusting the score.
    2. Choose the Promote (default) or Demote option.
    3. Use the slider to determine how much you want the metric to influence the score of the items.
  3. Attribute boosting:
    1. Click Add rule.
    2. Click the created rule to open its settings.
    3. In the Items scope section, click Define filter.
      Items that meet the filter will have their scores adjusted. To learn how to build filters, see Recommendation filters.
    4. Choose the Promote (default) or Demote option.
    5. Use the slider to determine how much you want the metric to influence the score of the items.
  4. Confirm the settings by clicking Apply.

Additional settings


  1. In the Additional settings section, click Define.
  2. If you want to exclude items that the customer already purchased, enable the Exclude already bought products option.
  3. If you want to sort the items, select a metric from the Sorting metric dropdown list.
  4. If the recommendation type you selected has default filters defined, you can disable them by using the Default filters toggle.
    Important: When the default filters and the filters defined in the recommendation are mutually exclusive, recommendation filters take precedence. You can find more information on configuration of default filters here.

Saving the recommendation


To save the recommendation:

  • as a draft, click Finish later.
  • and activate the recommendation, click Save.

Once you activate the recommendation, you can use it as described in the Distributing recommendations section.

πŸ˜•

We are sorry to hear that

Thank you for helping improve out documentation. If you need help or have any questions, please consider contacting support.

πŸ˜‰

Awesome!

Thank you for helping improve out documentation. If you need help or have any questions, please consider contacting support.

Close modal icon Placeholder alt for modal to satisfy link checker