Similar-Items recipe
The Similar-Items (aws-similar-items) generates recommendations for items that are similar to an item you specify. Similar-Items is optimized for similar item recommendation scenarios with item metadata. To use Similar-Items, you must create an Interactions dataset and an Items dataset. Use Similar-Items when your catalog has item metadata and items with little to no interactions, but your Interactions dataset still has at minimum 1000 unique historical and event interactions (combined).
Similar-Items calculates similarity based on both the co-occurrence of the item in user histories in your Interaction dataset, and the item metadata, including categorical and unstructured text metadata, in your Items dataset. For example, with Similar-Items Amazon Personalize could recommend items customers frequently bought together with a similar style, or movies that different users also watched with a similar description.
With Similar-Items, you provide an item ID in a GetRecommendations operation (or the Amazon Personalize console) and Amazon Personalize returns a list of similar items. Or you can use a batch workflow to get similar items for all of the items in your inventory (see Getting batch recommendations and user segments). You can get recommendations for items that are similar to a cold item (an item with fewer than five interactions). If Amazon Personalize can't find the item ID that you specify in your recommendation request or batch input file, the recipe returns popular items as recommendations.
After you create a solution version, make sure you keep your solution version and data up to date. With Similar-Items, you must manually create a new solution version (retrain the model) to reflect updates to your catalog and update the model with your user’s most recent behavior. For more information, see Maintaining recommendation relevance.
For information on formatting categorical and unstructured text metadata in your Items dataset, see Item data. If you don't have item metadata and want to recommend similar items, use the SIMS recipe.
Properties and hyperparameters
The Similar-Items recipe has the following properties:
-
Name –
aws-similar-items -
Recipe Amazon Resource Name (ARN) –
arn:aws:personalize:::recipe/aws-similar-items -
Algorithm ARN –
arn:aws:personalize:::algorithm/aws-similar-items
For more information, see Step 1: Choosing a recipe.
The following table describes the hyperparameters for the Similar-Items recipe. A hyperparameter is an algorithm parameter that you can adjust to improve model performance. Algorithm hyperparameters control how the model performs. 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 | |
item_id_hidden_dimension |
The number of hidden variables Amazon Personalize uses to model item ID embeddings based
on interactions data. Hidden variables recreate users'
purchase history and item statistics to generate ranking scores.
To use
To use HPO, set Default value: 100 Range: [30, 200] Value type: Integer HPO tunable: Yes |
item_metadata_hidden_dimension |
The number of hidden variables Amazon Personalize uses to model item metadata.
To use
To use HPO, set Default value: 100 Range: [30, 200] Value type: Integer HPO tunable: Yes |
