HRNN-Coldstart recipe (legacy) - Amazon Personalize

HRNN-Coldstart recipe (legacy)

Note

Legacy HRNN recipes are no longer available. This documentation is for reference purposes.

We recommend using the aws-user-personalizaton (User-Personalization) recipe over the legacy HRNN recipes. User-Personalization improves upon and unifies the functionality offered by the HRNN recipes. For more information, see User-Personalization recipe.

Use the HRNN-Coldstart recipe to predict the items that a user will interact with when you frequently add new items and interactions and want to get recommendations for those items immediately. The HRNN-Coldstart recipe is similar to the HRNN-Metadata recipe, but it allows you to get recommendations for new items.

In addition, you can use the HRNN-Coldstart recipe when you want to exclude from training items that have a long list of interactions either because of a recent popularity trend or because the interactions might be highly unusual and introduce noise in training. With HRNN-Coldstart, you can filter out less relevant items to create a subset for training. The subset of items, called cold items, are items that have related interaction events in the Item interactions dataset. An item is considered a cold item when it has the following:

  • Fewer interactions than a specified number of maximum interactions. You specify this value in the recipe's cold_start_max_interactions hyperparameter.

  • A shorter relative duration than the maximum duration. You specify this value in the recipe's cold_start_max_duration hyperparameter.

To reduce the number of cold items, set a lower value for cold_start_max_interactions or cold_start_max_duration. To increase the number of cold items, set a greater value for cold_start_max_interactions or cold_start_max_duration.

HRNN-Coldstart has the following cold item limits:

  • Maximum cold start items: 80,000

  • Minimum cold start items: 100

If the number of cold items is outside this range, attempts to create a solution will fail.

The HRNN-Coldstart recipe has the following properties:

  • Name – aws-hrnn-coldstart

  • Recipe Amazon Resource Name (ARN) – arn:aws:personalize:::recipe/aws-hrnn-coldstart

  • Algorithm ARN – arn:aws:personalize:::algorithm/aws-hrnn-coldstart

  • Feature transformation ARN – arn:aws:personalize:::feature-transformation/featurize_coldstart

  • Recipe type – USER_PERSONALIZATION

For more information, see Choosing a recipe.

The following table describes the hyperparameters for the HRNN-Coldstart recipe. A hyperparameter is an algorithm parameter that you can adjust to improve model performance. Algorithm hyperparameters control how the model performs. Featurization hyperparameters control how to filter the data to use in training. The process of choosing the best value for a hyperparameter is called hyperparameter optimization (HPO). For more information, see Hyperparameters and HPO.

The table also provides the following information for each hyperparameter:

  • Range: [lower bound, upper bound]

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

  • HPO tunable: Can the parameter participate in HPO?

Name Description
Algorithm hyperparameters
hidden_dimension

The number of hidden variables used in the model. Hidden variables recreate users' purchase history and item statistics to generate ranking scores. Specify a greater number of hidden dimensions when your Item interactions dataset includes more complicated patterns. Using more hidden dimensions requires a larger dataset and more time to process. To decide on the optimal value, use HPO. To use HPO, set performHPO to true when you call CreateSolution and CreateSolutionVersion operations.

Default value: 149

Range: [32, 256]

Value type: Integer

HPO tunable: Yes
bptt

Determines whether to use the back-propagation through time technique. Back-propagation through time is a technique that updates weights in recurrent neural network-based algorithms. Use bptt for long-term credits to connect delayed rewards to early events. For example, a delayed reward can be a purchase made after several clicks. An early event can be an initial click. Even within the same event types, such as a click, it’s a good idea to consider long-term effects and maximize the total rewards. To consider long-term effects, use larger bptt values. Using a larger bptt value requires larger datasets and more time to process.

Default value: 32

Range: [2, 32]

Value type: Integer

HPO tunable: Yes
recency_mask

Determines whether the model should consider the latest popularity trends in the Item interactions dataset. Latest popularity trends might include sudden changes in the underlying patterns of interaction events. To train a model that places more weight on recent events, set recency_mask to true. To train a model that equally weighs all past interactions, set recency_mask to false. To get good recommendations using an equal weight, you might need a larger training dataset.

Default value: True

Range: True or False

Value type: Boolean

HPO tunable: Yes
Featurization hyperparameters
cold_start_max_interactions

The maximum number of user-item interactions an item can have to be considered a cold item.

Default value: 15

Range: Positive integers

Value type: Integer

HPO tunable: No
cold_start_max_duration

The maximum duration in days relative to the starting point for a user-item interaction to be considered a cold start item. To set the starting point of the user-item interaction, set the cold_start_relative_from hyperparameter.

Default value: 5.0

Range: Positive floats

Value type: Float

HPO tunable: No
cold_start_relative_from

Determines the starting point for the HRNN-Coldstart recipe to calculate cold_start_max_duration. To calculate from the current time, choose currentTime.

To calculate cold_start_max_duration from the timestamp of the latest item in the Item interactions dataset, choose latestItem. This setting is useful if you frequently add new items.

Default value: latestItem

Range: currentTime, latestItem

Value type: String

HPO tunable: No
min_user_history_length_percentile

The minimum percentile of user history lengths to include in model training. History length is the total amount of data about a user. Use min_user_history_length_percentile to exclude a percentage of users with short history lengths. Users with a short history often show patterns based on item popularity instead of the user's personal needs or wants. Removing them can train models with more focus on underlying patterns in your data. Choose an appropriate value after you review user history lengths, using a histogram or similar tool. We recommend setting a value that retains the majority of users, but removes the edge cases.

For example, setting min__user_history_length_percentile to 0.05 and max_user_history_length_percentile to 0.95 includes all users except those with history lengths at the bottom or top 5%.

Default value: 0.0

Range: [0.0, 1.0]

Value type: Float

HPO tunable: No
max_user_history_length_percentile

The maximum percentile of user history lengths to include in model training. History length is the total amount of data about a user. Use max_user_history_length_percentile to exclude a percentage of users with long history lengths because data for these users tend to contain noise. For example, a robot might have a long list of automated interactions. Removing these users limits noise in training. Choose an appropriate value after you review user history lengths using a histogram or similar tool. We recommend setting a value that retains the majority of users but removes the edge cases.

For example, setting min__user_history_length_percentile to 0.05 and max_user_history_length_percentile to 0.95 includes all users except those with history lengths at the bottom or top 5%.

Default value: 0.99

Range: [0.0, 1.0]

Value type: Float

HPO tunable: No