Choose this use-case if you wish to develop a model that tailors search results based on a user's behavior, or past interactions. Personalized search may be useful in E-commerce, job portals, or travel platforms. 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 a personalized list of search results.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Personalized Search model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Personalized Search model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Evaluating PredictionsThis section contains a quick model evaluation guide that helps you understand how well your model is performing.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.