Color is one of those features that significantly affects the purchase decisions in the fashion industry, but should not limit the choice. You can recommend items which are similar to the currently viewed by a customer, but you can add a little twist to that by elastically filtering the items in the recommendation based on the favorite color of your customer. For further details read the instruction in this use case. ## Prerequisites --- - Enable the [similar recommendation model](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-recommendations). - Supplement customer profiles with the favorite color attribute. ## Create a recommendation --- 1. Go to

**AI Hub > (AI Recommendations) Models > Add recommendation**. 2. Enter a meaningful name of the recommendation. 3. In the **Type & Items feed** section, click **Define**. 1. From the **Items feed** dropdown list, select the catalog that contains items for the recommendation. 2. As the type, select **Similar**. 3. Click **Apply**. 4. In the **Items** section, click **Define**. 5. Click **Add slot**. You can name the slot for later reference. 5. In the **Number of items** subsection, set the minimum and maximum number of items to `2`.

Setting the minimum and maximum number of items to the same number ensures that exactly this many items will appear in the slot.

6. Click **Elastic filter**.

Learn about the difference among [elastic, static filters](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign#select-conditions-of-displaying-items), and [distinct filters](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign#distinct-filter).

7. From the dropdown list, choose **Visual Builder**. 7. Click **Select attribute**. 7. From the dropdown list, choose the item color attribute. 8. Click **Operator**. 9. From the dropdown menu, choose **Equals**. 9. Click the

icon and keep clicking until you get the

option. 10. Click **Select value**. 11. From the dropdown list, choose the attribute that contains the customer's favorite color.

11. At the bottom of the elastic filter pop-up, click **Apply**. 12. In the **Items** section, click **Apply**. 1. In **Boosting**, you can enable [boosting](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign#define-the-boosting-factors). 13. In **Additional settings**, optionally you can exclude already bought products and set a metric to sort by. 14. Save the recommendation by clicking **Save**. ## What's next --- You can display the recommendation to customers in a number of ways, for example by using the [recommendation insert](/developers/inserts/recommendations-v2) in [dynamic content](/docs/campaign/dynamiccontent/creating-dynamic-content). ## Read more --- - [Creating recommendations](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign) - [Filters in recommendations](/docs/ai-hub/recommendations-v2/recommendation-filters) - [Requirements for item feed](/docs/ai-hub/recommendations-v2/item-feed-requirements)