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.

Distributing recommendations


You can use the ID of the recommendation and put it in the form of an insert 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,
  • web push - this way you can send out recommended items in notifications on your website.
  • mobile application - you can use documents to build your own mobile app and show the recommended items.

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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 products 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 indicating 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 a trained product feed.
  3. Below, select the recommendation type. If the recommendations are greyed out, it means the AI engine is not trained yet.
    Note: To get information about recommendation types, click here.
  4. Confirm the settings by clicking Apply.

Select an aggregate


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 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 Training 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
Important: The recommendation engine only provides data. The scope of item attributes you want to display on your website and the way you present the recommendation frame is configurable outside Synerise.

Configure item settings


The next step is to define the items to be displayed in the recommendation frame.

Managing recommendations slots

You can use slots to assign space in your recommendation frame to specific items. In the settings of a single slot, you can declare the number of items and filter rules according to which the items will be displayed.

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

Such possibility to granularly set multiple filter rules on the groups of items allows you to display subset of items in a recommendation frame according to your strategies.

For example, you can use three slots to display:

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

Additionally, you can define the order of displaying slots by ticking the Keep slots in order. The numbers next to the name of the slot indicate their order. If you don’t use the option, the items will be displayed according to their score in the recommendation and boosting factors.

Note: If there are no items that match the filters, the slots will remain empty, however, the empty space won’t be visible on the interface.

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

Define the number of displayed items

  1. In the Number of items section:
    1. In the Minimum field, enter the lowest number of items displayed in the recommendation frame.
    2. In the Maximum field, enter the highest number of items displayed in the recommendation frame.
    3. If you don’t want to display the items a customer has already purchased, switch the Exclude already bought products toggle on.

Select conditions of displaying items

  1. To narrow down the scope of the items displayed in the recommendation frame, click Define filters.
    1. Select one of the filter creators:
      • visual builder
      • IQL query wizard
        Elastic filters explained

        Apart from selecting the items to be displayed in the recommendation frame, the elastic filter supplements the recommendation frame 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 to be displayed in the recommendation frame, then the empty slots in the frame are replaced with personalized items.

        Static filters explained

        When you use a static filter, it shows the fixed number of items that match the conditions of the filter. If there is not enough items to fill in the recommendation frame (the number you entered in the step 3), the recommendation frame is not displayed at all.

WARNING: We don’t recommend using Elastic filters or Static filters at the same time.

Increase the recommendation variety

  1. To manage the variety of items in the recommendation frame, in the Distinct filter section, click Define filters.

    Important: This filter can only use attributes defined in configuring the recommendation engine.

    1. From the list, select an attribute.
    2. In the Max no. of occurrences, define the number of items with the same value of the attribute (for example, a brand) that can be displayed in the recommendation frame.
      Result:
    A blank form for configuring recommendation campaigns
    Attributes defined in the distinct filters

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

    Distinct filter explained

    Distinct filters allow you to increase the variety of items displayed in the recommendation frame. 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 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 display in the recommendation frame.
    • 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 display in the recommendation frame.

Define the boosting factors

You can influence the arrangement of items in the recommendation, which is by default arranged according to a score given by the model (the type of recommendation selected). You can boost the items in the recommendation by means of two factors:

  • Boosting - It allows you to select the top items according to the chosen metric out of the items which meet the conditions defined in the elastic/static/distinct filters.
  • Sorting - It allows you to define the order of the items in a particular order.
  1. In the Additional settings section, select the option:

    • Boosting the score of your items:
      1. Choose one of 5 metrics.
      2. Determine how much you want the metric to influence the score of the items by using a slider.
    • Sorting the order of items according to a metric:
      1. Choose one of 5 metrics.
      2. Determine how much you want the metric to influence the score of the items by using a slider.
    Metrics explained

    • Sold items count in the last 30 days - The system favors the items that were sold in the highest quantity in the last 30 days.
    • Sold items value in the last 30 days - The system favors the items which summed value, that is how many were sold but also how much they cost, is the highest in the last 30 days.
    • Page visit count in the last 30 days - The system favors your popular items, based on the amount of times the items’ page was visited in the last 30 days.
    • Conversion percent in the last 30 days - The system favors those items which conversion rate in the last 30 days is the highest.
    • Conversion percent after clicking in the recommendation in the last 30 days - The system favors those items which conversion rate after clicking in the recommendation is the highest in the last 30 days.
    • Sold items count in the last day - The system favors the items which were sold most frequently the day before.
    • Sold items count from the same weekday last week - The system favors those items which were sold most frequently during the same weekday, a week before.

  2. Confirm the settings by clicking Apply.

  3. 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.
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