> For the complete documentation index, see [llms.txt](https://docs.dinmo.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dinmo.io/intelligence/product-recommendations.md).

# Product Recommendations

Turn user–item interactions into ranked recommendations and campaign audiences. Items can be products, deals, donation campaigns, or other entities represented in your catalog.

## What you can do

* Recommend relevant items to each user.
* Find items related to another item.
* Find an audience for each item, vendor, or category.
* Rank a user's vendor or category preferences.
* Allocate one campaign audience between targets, with one assignment per customer and a separate recommendation ranking.

The distinction between audiences matters: independent lists per offer can overlap; allocation assigns each selected customer once. Neither an affinity score nor a revenue-based allocation weight is a conversion or revenue forecast.

## How it works

An intelligence learns from your user, item, and interaction models. Recommendation scenarios reuse that trained model with their own scopes, objectives, delivery rules, and schedules. The interface calls these scenarios predictions.

Choose the business identity carefully: recommending a deal is not the same as recommending its vendor. Restrict sources to the population you want to serve and candidates to the items or users allowed in the result.

Results are published to your warehouse and exposed through DinMo models. Review publication, coverage, freshness, and evidence before activating them. Technical success, offline relevance, and campaign impact are separate checks.

## Documentation guide

| Start here                                                                                                | What you will learn                                                            |
| --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
| [Prepare your data](/intelligence/product-recommendations/prepare-your-data.md)                           | Technical prerequisites, required models, optional mappings, and data quality. |
| [Choose a recommendation use case](/intelligence/product-recommendations/recommendation-use-cases.md)     | Output types, source and candidate segments, and item availability.            |
| [Build and allocate audiences](/intelligence/product-recommendations/audiences-and-allocation.md)         | Overlap, unique assignments, weighted distribution, and companion rankings.    |
| [Configure and refresh](/intelligence/product-recommendations/configure-and-refresh.md)                   | The main configuration choices, training, refresh, and delivery rules.         |
| [Understand performance](/intelligence/product-recommendations/understand-performance.md)                 | Coverage, evaluation metrics, score limitations, and commercial validation.    |
| [Use results and best practices](/intelligence/product-recommendations/use-results-and-best-practices.md) | Output fields, activation checks, and keeping computation focused.             |


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