Recording real-time events to influence recommendations
An event is an interaction between a user and your catalog. It can be an interaction with an item, such as a user purchasing an item or watching a video, or it can be taking an action, such as applying for a credit card or enrolling in a membership program.
Amazon Personalize can make recommendations based on real-time event data only, historical event data only, or a mixture of both. Record real-time events as your customers interact with recommendations. This builds out your interactions data and keeps your data fresh. And it tells Amazon Personalize about the current interests of your user, which can improve recommendation relevance.
If your domain use case or custom recipe supports real-time personalization, Amazon Personalize uses events in real time to update and adapt recommendations according to a user's evolving interest.
How you record real-time events depends on the type of interactions data you are importing:
-
For item interactions, you record real-time events with the PutEvents API operation. Amazon Personalize appends this data to the item interaction data in your dataset group. For more information, see Recording real-time item interaction events.
-
For action interactions, you record real-time events with the PutActionInteractions API operation. Amazon Personalize appends this data to the Action interactions dataset in your dataset group. Only the PERSONALIZED_ACTIONS recipes use action interactions data. For more information, see Recording real-time action interaction events.
Topics
How real-time events influence recommendations
If your recipe supports real-time personalization, after you create a recommender or custom campaign, Amazon Personalize uses new recorded event data for existing items or actions within seconds of import. The following use cases and recipes support real-time personalization:
If you use the Trending-Now recipe, Amazon Personalize automatically considers items from new events data over configurable intervals. You don't have to create a new solution version. For more information, see Trending-Now recipe.
If the item, action, or user in the event is new, how the Amazon Personalize uses the data depends on your use case or recipe. For more information, see Updating data in datasets after training.
Recording events for anonymous users
Important
If you don't record at minimum one event with a sessionId and userId for a user, Amazon Personalize won't use
the activity tracked to only the sessionId when training. And after training completes, recommendations will
no longer be based on activity tracked to the sessionId.
You can record item interaction or action interaction events for users before they create an account. Record events for anonymous users to build a continuous event history with events from before and after they log in. This provides Amazon Personalize more interactions data about the user, which can help generate more relevant recommendations.
To record events for anonymous users (users that haven't logged in), for each event specify only a sessionId.
Your application generates a unique sessionId when a user first visits your website or uses your application.
You must use the same sessionId in all events throughout the session.
Amazon Personalize uses the
sessionId to associate events with the user before they log in.
Amazon Personalize doesn't use events from anonymous users when training until you associate them with a userId. For
more information, see Building a continuous event history for anonymous users.
To provide real-time personalization for anonymous users,
specify the sessionId as the userId in your GetRecommendations or GetActionRecommendations request.
-
For a code samples that show how to record item interaction events with the PutEvents operation and a sessionId and userId, see Recording a single item interaction event.
-
For a code samples that show how to record action interaction events with the PutActionInteractions operation and a sessionId and userId, see Recording a single action interaction event.
Building a continuous event history for anonymous users
To build an event history for an anonymous user
and have Amazon Personalize use their events when training, record at minimum one
event with both a sessionId and a userId. Then you can record any number of events for the
userId. After you start providing a userId, the sessionId can change. During the
next full retraining, Amazon Personalize associates the userId with the
anonymous user history tracked to the original sessionId.
After retraining completes, recommendations will be based on activity
tracked to both the sessionId from the anonymous events and any events
tracked to their userId.
Note
If your user doesn't create an account and you want Amazon Personalize to use the data when training, you can use the
sessionId as the userId in events. However, if the user eventually creates an account, you
won't be able to associate the events from their anonymous browsing with their new userId.
Third-party event tracking services
The following Customer Data Platforms (CDPs) can help you collect event data from your application and send it to Amazon Personalize.
-
Amplitude – You can use Amplitude to track user actions to help you understand your users' behavior. For information on using Amplitude and Amazon Personalize, see the following AWS Partner Network (APN) blog post: Measuring the Effectiveness of Personalization with Amplitude and Amazon Personalize
. -
mParticle – You can use mParticle to collect event data from your app. For an example that shows how to use mParticle and Amazon Personalize to implement personalized product recommendations, see How to harness the power of a CDP for machine learning: Part 2
. -
Segment – You can use Segment to send your data to Amazon Personalize. For more information on integrating Segment with Amazon Personalize, see Amazon Personalize Destination
.
Sample implementations
For a sample Jupyter notebook that shows how to use Amazon Personalize to react to
real-time behavior of users using an event tracker and the PutEvents
operation, see 2.View_Campaign_And_Interactions.ipynb
For an example that shows how to stream events from users interacting with recommendations, see streaming_events
For a complete example that contains the source code and supporting
files to deploy real-time APIs that sit between your Amazon Personalize resources and
client applications, see Real-Time
Personalization APIs
-
User context and user event collection
-
Response caching
-
Decorating recommendations based on item metadata
-
A/B testing
-
API authentication
