ECOMMERCE use cases - Amazon Personalize
Most viewedBest sellersFrequently bought togetherCustomers who viewed X also viewedRecommended for you
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ECOMMERCE use cases

The following sections list the requirements and Amazon Resource Name (ARN) for each ECOMMERCE use case. For all use cases, your interactions data must have the following:

  • At minimum 1000 item interactions records from users interacting with items in your catalog. These interactions can be from bulk imports, or streamed events, or both.

  • At minimum 25 unique user IDs with at least two item interactions for each.

For quality recommendations, we recommend that you have at minimum 50,000 item interactions from at least 1,000 users with two or more item interactions each.

Note

If you use the CreateRecommender API, provide the ARN listed here for the recipe ARN.

Most viewed

Get recommendations for popular items based on how many times your customers viewed an item.

  • Recipe ARN: arn:aws:personalize:::recipe/aws-ecomm-popular-items-by-views

  • GetRecommendations requirements:

    userId: Required

    itemId: Not used

    inputList: NA

  • Datasets used when training: Only Item interactions dataset (required)

  • Required event types: At minimum, 1000 View events.

Best sellers

Get recommendations for popular items based on how many times your customers purchased an item.

  • Recipe ARN: arn:aws:personalize:::recipe/aws-ecomm-popular-items-by-purchases

  • GetRecommendations requirements:

    userId: Required

    itemId: Not used

    inputList: NA

  • Datasets used when training: Only Item interactions dataset (required)

  • Required event types: At minimum, 1000 Purchase events.

Frequently bought together

Get recommendations for items that customers frequently buy together along with an item that you specify.

  • Recipe ARN: arn:aws:personalize:::recipe/aws-ecomm-frequently-bought-together

  • GetRecommendations requirements:

    userId: Required only if you filter by CurrentUser

    itemId: Required

    inputList: NA

  • Datasets used when training: Only Item interactions dataset (required)

  • Required event types: At minimum, 1000 Purchase events.

Customers who viewed X also viewed

Get recommendations for items that customers also viewed based on an item that you specify. With this use case, Amazon Personalize automatically filters items the user purchased based on the userId that you specify and Purchase events. If you apply your own filter, your filter is applied after the items the user already purchased are filtered out.

When filtering, Amazon Personalize considers at most 100 item interactions per user per event type. This applies to any automatic or custom filters. You can use the Service Quotas console to request an increase for this limit. For more information, see the Requesting a quota increase section of the Service Quotas User Guide. If you don't import item interactions for a user for three months, your filters no longer consider the user's historical data. To consider this data, you must import the user's entire event history again.

  • Recipe ARN: arn:aws:personalize:::recipe/aws-ecomm-customers-who-viewed-x-also-viewed

  • GetRecommendations requirements:

    userId: Required

    itemId: Required

    inputList: NA

  • Datasets used when training: Only Item interactions dataset (required)

  • Required event types: At minimum, 1000 View events.

  • Recommended event types: Purchase events.

Get personalized recommendations for items based on a user that you specify. With this use case, Amazon Personalize automatically filters out items the user purchased based on the userId that you specify and Purchase events. If you apply your own filter, your filter is applied after the items the user already purchased are filtered out.

When filtering, Amazon Personalize considers at most 100 item interactions per user per event type. This applies to any automatic or custom filters. You can use the Service Quotas console to request an increase for this limit. For more information, see the Requesting a quota increase section of the Service Quotas User Guide. If you don't import item interactions for a user for three months, your filters no longer consider the user's historical data. To consider this data, you must import the user's entire event history again.

When recommending items, this use case uses real-time-personalization and exploration. And it uses automatic updates to consider new items for recommendations.

  • Recipe ARN: arn:aws:personalize:::recipe/aws-ecomm-recommended-for-you

  • GetRecommendations requirements:

    userId: Required

    itemId: Not used

    inputList: NA

  • Datasets used when training:

    • Interactions (required)

    • Items (optional)

    • Users (optional)

  • Required number of events: At minimum, 1000 events.

  • Recommended event types: View and Purchase events.

  • Exploration configuration parameters: When you create a recommender, you can configure exploration with the following.

    • Emphasis on exploring less relevant items (exploration weight) – Configure how much to explore. Specify a decimal value between 0 to 1. The default is 0.3. The closer the value is to 1, the more exploration. With more exploration, recommendations include more items with less item interactions data or relevance based on previous behavior. At zero, no exploration occurs and recommendations are based on current data (relevance).

    • Exploration item age cutoff – Specify the maximum item age in days since the latest interaction across all items in the Item interactions dataset. This defines the scope of item exploration based on item age. Amazon Personalize determines item age based on its creation timestamp or, if creation timestamp data is missing, item interactions data. For more information how Amazon Personalize determines item age, see Creation timestamp data.

      To increase the items Amazon Personalize considers during exploration, enter a greater value. The minimum is 1 day and the default is 30 days. Recommendations might include items that are older than the item age cut off you specify. This is because these items are relevant to the user and exploration didn't identify them.