If you know what the customer's favorite brand is (for example, from a poll), you can adjust the recommendations to include that brand more often. This can be done by using **recommendation boosting** to promote items from that brand. Boosting rules are built using the same editor as the filters, but unlike filtering, boosting does not entirely exclude items that do not meet the conditions - it only tells the AI model to assign more weight to the `brand` parameter when calculating the final recommendation score (relevance to a particular customer) of an item. This article explains how to build a filter that matches the item's brand (item attribute) with the customer's favorite brand (customer profile attribute). You can use the mechanism for different attributes that you collect in customer's profiles, such as size, favorite color, favorite style, and so on. ## Prerequisites --- - **Recommended**: Become familiar with [creating recommendations](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign). This article does not explain every step of creating a recommendation in detail. - The item catalog must include an attribute that denotes the brand. - The customer profile must include an attribute that denotes the customer's favorite brand. In this example, the attribute is called `favoriteBrand`. ## Creating the recommendation --- ### Choose source, type, and add slots 1. Go to

**AI Hub > (AI Recommendations) Models > Add recommendation**. 2. In the **Type & Items feed** section: 1. Select an item catalog. 2. Select a recommendation type.

Boosting can be used with all recommendation types.

3. Click **Apply**. 3. In the **Items** section, configure at least one slot and click **Apply**. ### Build the boosting rule In this part of the process, you build a rule that promotes an item if its brand matches the `favoriteBrand` attribute in the customer's profile. 1. In the **Boosting** section, click **Define**. 2. Click **Add rule**. 3. Click **Define rule** and select **Visual Builder**. **Result** The Visual Builder window opens. 4. From the **Select attribute** drop-down list, select the brand attribute. You can use the search field. 5. From the **Operator** drop-down list, select **Equals**. 6. Click the value type icon (

) a few times until it changes to the customer value icon (

). 7. From the **Select value** drop-down list, select **favoriteBrand**. 8. Click **Apply**. 9. In the **Promote/Demote** selector, select **Promote** (default value). 12. Use the slider to adjust how much you want the rule to affect the results.

Screenshot of the boosting strength slider — The boosting strength slider

13. Save the **Boosting** section settings by clicking **Apply**.

After applying the settings, you can use the **Preview** tab (available in the upper left part of the recommendation creator screen) to see how your rule changed the recommendation result. If necessary, you can return to the settings and adjust the boosting strength to meet your expectations.

A boosting filter that matches the customer's favorite brand

### Additional settings and saving 1. Configure the **Additional settings** section and click **Apply**. 2. Save the recommendation. ## What's next --- You can use the ID of the recommendation and [inject it with a snippet](/docs/assets/snippets) in other types of communication, such as: - [dynamic content](/docs/campaign/dynamiccontent) - this way you can show the recommendations on your website. - [email](/docs/campaign/e-mail) - this way you can send out recommended items through emails. - mobile application - you can use [documents](/docs/assets/documents) to build your own mobile app and show the recommended items. - [mobile push](/docs/campaign/Mobile) - you can send recommendations through notifications in your mobile application. - [web push](/docs/campaign/Webpush) - this way you can send notifications to your customers through a web browser. - [SMS](/docs/campaign/SMS) - this way you can reach your customers with recommendations on their mobile. ## Read more --- - [Predictions](/docs/ai-hub/predictions) - [Recommendations](/docs/ai-hub/recommendations-v2)