# Recommendation engine

> Recommendation engine - Explore how to use historical user purchase data in order to create a
> recommendation model, which will attempt to guess which products out of a basket of items the
> customer will be likely to purchase at a given point in time.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.586090+00:00` (UTC).

## Primary page

- [Recommendation engine](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/rec-engine.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Ecommerce_recommendation_engine/Recommendation%20Engine.ipynb)

The accelerator provided in this notebook trains a model on historical customer purchases in order to make recommendations for future visits. The DataRobot features that will be utilized in this notebook are multi-Label modeling and feature discovery. Together the resulting model can provide rank ordered suggestions of content, product, or services that a specific customer might like.

In the notebook, you will:

- Analyze the datasets required
- Create a multilabel dataset for training
- Connect to DataRobot
- Configure a feature discovery project
- Generate features and models
- Generate recommendations for new visits