AI Engine configuration

Before you can create your first recommendation, you must prepare a feed which will be the source of items displayed in the recommendations.

After that, you must select a recommendation type so the system can start the model training and you can send relevant and precise recommendations.

The next steps concern selecting the attributes for the model training, the filterable, and response attributes. All these steps are described in this article. After you perform them and the model training is completed, you can create a recommendation.

Select product feed

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The first step is selecting the feed from which recommendations will source the items. You can either select a catalog that contains a feed or use Google Merchant Feed.

WARNING: We recommend using Google Merchant XML instead of XML files due to the size limits (an XML file cannot exceed 10 MB).

1. Go to Settings > AI engine configuration.
2. Click Add feed.
   Result: A pop-up appears.
3. Select the product feed you want to use.
   • A product feed uploaded to a catalog (available in Data Management > Catalogs)
   • Google Merchant
     Product feed uploaded to a catalog

     1. From the list of catalogs, select a catalog.
     2. Confirm by clicking Apply.
        Result: The selected catalog appears on the list in Settings > AI Engine configuration.

     Google Merchant

     1. Provide the following information:
        • the link to the Google Merchant feed,
        • the name of the field,
        • the type of the feed,
        • the frequency the feed is to be updated,
        • authorization type,
        • user name,
        • and password.
     2. Confirm by clicking Apply.
        Result: Feed appears on the list.

Configuring model settings for the selected feed

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On the list of feeds, click the feed you added according to the Select product feed procedure.

Select catalog to be imported

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The Item catalog section is filled out automatically after you performed the steps in the Select product feed procedure.

Select the type of recommendations

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2. In the Recommendation models tab, click Show.
3. Select the type of recommendations you want to display to your customers.
   Based on the choice, the models will be trained to show your customers items according to the selected recommendation type.
   The description of each recommendation type is available at the beginning of the article.
   Important: If you selected Visual similarity or Similar items, you must define extra attributes (which are required for the model training) in the Training attributes section on the application interface.
4. Confirm your selection by clicking Apply.

Select response attributes

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A response attribute is any quality that can describe an item and it is visible to a customer (for example, the name of the item, price, description, size, and so on).

1. In the Response attributes tab, click Show.
2. Click Select attributes.
3. Select the checkboxes next to the attributes which you want to display to the customers.
   Note:

   There are two types of attributes:

   • Textual attributes: These are all attributes that can have one value, for example, a color, an item name, a brand name, fabric, pattern, and so on.
   • Range attributes: These are all attributes that can have numerical values within a selected range, such as size, price, width, length, and so on.
4. Confirm by clicking Apply.

Select filters

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Optionally, you can define the attributes which you can use later to filter recommendations results. The attributes also become available in the Analytics module and filters.

1. In the Filterable attributes tab, click Show.
2. Click Select attributes.
3. Tick the checkboxes next to the attributes which you want to use for filtering the recommendation results.
4. Confirm by clicking Apply.

Define the item link

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An item link is an attribute of an item to which Synerise’s UTM parameters are added.

1. In the Definition of item link tab, click Show.
2. From the Attribute dropdown list, select an attribute that will be an item link.
3. Confirm by clicking Apply.
   Result: Thesnrai, snr_content, and snr_id parameters are added to the URL of the item. For example: https://www.exemplary-shop.com./winter-shoes-camelbrown.html?snrai_campaign=QWERTY[…]e=&snrai_content=&snrai_id=123456789010305

Select training attributes

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Important: Define the training attributes only if you selected Visual similarity or Similar items in the Recommendation types tab.

1. In the Response attributes tab, click Show.
2. Click Select attributes.
3. Tick the checkboxes next to the attributes which you want to display to the customers.
   Note:

   There are two types of attributes:

   • Textual attributes: These are all attributes that can have one value, for example, a color, an item name, a brand name, fabric, pattern, and so on.
   • Range attributes: These are all attributes that can have numerical values within a selected range, such as size, price, width, length, and so on.
4. Confirm by clicking Apply.

Select attributes to increase item variety

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By using the distinct filters, you can define the number of items with the same value of the attribute (for example, a brand) that can be displayed in the recommendation frame.

1. In the Attributes for distinct filters tab, click Show.
2. Click Select attributes.
3. On the pop up, from the list, choose up to 5 attributes.
4. Click Apply.
5. Confirm the settings of the tab by clicking Apply.
   Result: These attributes are available in the Distinct filter while creating a campaingn.

Switch AI Search for the selected feed

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Optionally, you can enable the AI Search for the selected catalog or Google Merchant Feed.

1. In the Applied search engines tab, click Show.
2. Switch the Search engines toggle on.
3. Confirm by clicking Apply.

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