Amazon Personalize
Developer Guide

Personalized-Ranking Recipe

Personalized ranking. This predefined recipe has the following properties:

  • Nameaws-personalized-ranking

  • Recipe ARNarn:aws:personalize:::recipe/aws-personalized-ranking

  • Algorithm ARNarn:aws:personalize:::algorithm/aws-personalized-ranking

  • Feature Transformation ARNarn:aws:personalize:::feature-transformation/JSON-percentile-filtering

  • Recipe typePERSONALIZED_RANKING

The following table lists the hyperparameters used in the recipe. For each hyperparameter the name, default value, and description are given, as well as the following properties:

  • Range: [lower bound, upper bound]

  • Value type: Integer, Continuous (float), Categorical (boolean, list, string)

  • HPO tunable: Can the parameter participate in hyperparameter optimization (HPO)?

Name Default value Range Value type HPO tunable Description
Algorithm
hidden_dimension 149 [32, 256] integer Yes Number of hidden variables in the model.
bptt 32 [1, 32] integer No Backpropagation through time.
recency_mask true true/false boolean Yes

true: Models for temporal drift in user behavior.

false: Treats all past interactions the same.

Featurization
min_user_history_length_percentile 0.0 [0.0, 1.0] float No The minimum percentile of user history lengths to include in model training. The history length is the amount of available data for a user.
max_user_history_length_percentile 0.99 [0.0, 1.0] float No

The maximum percentile of user history lengths to include in model training.

For example, min_hist_length_percentile = 0.05 and max_hist_length_percentile = 0.95 includes all users except the bottom and top 5% with respect to their history lengths.