# Recommendation engine
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/rec-engine.html

> 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.

# Recommendation engine

[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

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