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 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 interactions for each.

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

• Required datasets: Interactions

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

Best sellers

Get recommendations for popular items based on how many times that 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

• Required datasets: Interactions

• 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

• Required datasets: Interactions

• 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.

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

• GetRecommendations requirements:

  userId: Required

  itemId: Required

  inputList: NA

• Required datasets: Interactions

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

• Recommended event types: Purchase events.

Recommended for you

Get personalized recommendations for items based on a user 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. For better performance, include Purchase events along with the required View events.

When recommending items, this use case uses exploration. Exploration involves testing different item recommendations to learn how users respond to items with very little interaction data. You can configure exploration when you create your recommender.

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

• GetRecommendations requirements:

  userId: Required

  itemId: Not used

  inputList: NA

• Required datasets: Interactions

• 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 (for APIs, this is called explorationWeight in the RecommenderConfig) – 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 interactions data or relevance. 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 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, 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.