Fine-tune results

Important

Azure Intelligent Recommendations Retirement and Migration Plan

Azure Intelligent Recommendations will be retired on March 31, 2026.

We're retiring the product due to changes in strategy and focus with the onset of the copilot era. Currently, there are no equivalent services available within the Microsoft stack, so we can't offer a migration plan to a similar service.

Next steps:

• Identify or build an alternative.
• Migrate all your traffic to the chosen alternative.
• Stop calling the Recommendations endpoint before the shutdown date.
• Delete all service endpoints, modeling, and IR accounts in the Azure portal before the shutdown date.

We'll continue to monitor and maintain Azure Intelligent Recommendations during the retirement period. After the product is retired, we'll keep the service data for three months before deleting it.

Intelligent Recommendations provides capabilities that can help deliver high-performing results. Building a recommendations system usually requires in-house expertise, but Intelligent Recommendations offers features that let you get started quickly, and then modify it for your situation.

Flexible filtering

Intelligent Recommendations, as a generic recommendations solution, can be customized by our customers to set filtering logic as needed for their implementation. In most retail solutions, products can be limited by size, style, and color. These limits might not always align with the full selection of products that retailers sell, resulting in wasted filtering opportunities. With Intelligent Recommendations, you can classify filters that work for your content without the constraints of color, market, or style. In addition, you decide what filters are visible to your consumers. This feature enhances relevance for products and creates a better discovery experience for consumers.

For example:

• Mattress stores can choose to filter by comfort level or mattress feel
• PC accessories can be filtered by compatibility, as decided by the retailer
• Clothing can be filtered by season, pattern, or multiple style indicators

Show top candidates for recommendations lists

Sometimes a product is so important that curators want the ability to boost those products to the front of recommendations results. Intelligent Recommendations supplies configurable functionality that allows curators, merchandisers, and editors to reserve top spots for curated picks or most popular items. You can then populate these lists by using the recommendation algorithm of your choice.

See also

Use personalized recommendations lists
Provide contextual recommendations lists
Provide trending recommendations lists