Use Case Documentation
Personalized Recommendations

Choose this use-case if you wish to develop a model that provides tailored content or suggestions based on a user's behavior and past interactions. Personalized recommendations may be useful in E-commerce, media and entertainment, or travel. Given a dataset of time-based user-item interactions, an optional dataset of catalog attributes, and an optional dataset of user attributes, you can generate a model that presents users with personalized content.

Dataset and Feature Group Requirements

This section specifies the Datasets / Feature Groups requirements to successfully train a Personalized Recommendations model. Feature requirements include recommendations on additional datasets that might enhance model performance.

Training Models - Training Options and Metrics

This section describes all the available model training options that can be used to create a Personalized Recommendations model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.

Evaluating Predictions

This section contains a quick model evaluation guide that helps you understand how well your model is performing.

Prediction API

This section discusses the prediction API method so that you could properly generate predictions from the model you deployed.