SIMS recipe - Amazon Personalize
SIMS sample notebook

SIMS recipe

Note

All RELATED_ITEMS recipes use interactions data. Choose SIMS if you want to configure more hyperparameters for the model. Choose the Similar-Items recipe if you have item metadata and want Amazon Personalize to use it to find similar items.

The Item-to-item similarities (SIMS) recipe uses collaborative filtering to recommend items that are most similar to an item you specify when you get recommendations. SIMS uses your Item interactions dataset, not item metadata such as color or price, to determine similarity. SIMS identifies the co-occurrence of the item in user histories in your Interaction dataset to recommend similar items. For example, with SIMS Amazon Personalize could recommend coffee shop items customers frequently bought together or movies that different users also watched.

When you get similar item recommendations, you can filter the items based on an attribute of the item you specify in your request. You do this by adding a CurrentItem.attribute element to your filter. For an example, see item data filter examples.

To use SIMS, you must create an Item interactions dataset with at least 1000 unique historical and event interactions (combined). SIMS doesn't use data in a Users or Items dataset when generating recommendations. You can still filter recommendations based on data in a these datasets. For more information, see Filtering recommendations and user segments.

If there isn't sufficient user behavior data for an item or the item ID you provide isn't found, SIMS recommends popular items. After you create a solution version, make sure you keep your solution version and data up to date. With SIMS, you must manually create a new solution version (retrain the model) for Amazon Personalize to consider new items for recommendations and update the model with your user’s most recent behavior. Then you must update any campaign using the solution version. For more information, see Maintaining recommendation relevance.

The SIMS recipe has the following properties:

  • Name – aws-sims

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

  • Algorithm ARN – arn:aws:personalize:::algorithm/aws-sims

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

  • Recipe type – RELATED_ITEMS

The following table describes the hyperparameters for the SIMS 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 hyperparameter optimization (HPO)?

Name Description
Algorithm hyperparameters
popularity_discount_factor

Configure how popularity influences recommendations. Specify a value closer to zero to include more popular items. Specify a value closer to one for less emphasis on popularity.

Default value: 0.5

Range: [0.0, 1.0]

Value type: Float

HPO tunable: Yes
min_cointeraction_count

The minimum number of co-interactions you need to calculate the similarity between a pair of items. For example, a value of 3 means that you need three or more users who interacted with both items for the algorithm to calculate their similarity.

Default value: 3

Range: [0, 10]

Value type: Integer

HPO tunable: Yes
Featurization hyperparameters
min_user_history_length_percentile

The minimum percentile of user history lengths to include in model training. History length is the total amount of available data on 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.

Default value: 0.005

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 available data on a user. Use max_user_history_length_percentile to exclude a percentage of users with long history lengths. Users with a long history 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, min_hist_length_percentile = 0.05 and max_hist_length_percentile = 0.95 includes all users except ones with history lengths at the bottom or top 5%.

Default value: 0.995

Range: [0.0, 1.0]

Value type: Float

HPO tunable: No
min_item_interaction_count_percentile

The minimum percentile of item interaction counts to include in model training. Use min_item_interaction_count_percentile to exclude a percentage of items with a short history of interactions. Items with a short history often are new items. Removing them can train models with more focus on items with a known history. 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 items, but removes the edge cases.

Default value: 0.01

Range: [0.0, 1.0]

Value type: Float

HPO tunable: No
max_item_interaction_count_percentile

The maximum percentile of item interaction counts to include in model training. Use max_item_interaction_count_percentile to exclude a percentage of items with a long history of interactions. Items with a long history tend to be older and might be out of date. For example, a movie release that is out of print. Removing these items can focus on more relevant items. 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 items but removes the edge cases.

For example, min_item_interaction_count_percentile = 0.05 and max_item_interaction_count_percentile = 0.95 includes all items except ones with an interaction count at the bottom or top 5%.

Default value: 0.9

Range: [0.0, 1.0]

Value type: Float

HPO tunable: No

SIMS sample notebook

For a sample Jupyter notebook that shows you how to use the SIMS recipe, see Finding similar items + HPO.