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I want to feed real time data into aws personalize to build a recommendation engine. I've read online resources and in those guides, I could see that the training user-interaction data, user data and item data is provided in the beginning while creating the recommendation engine.

However, I have an app and I will gather data in the app and want to feed those realtime data into aws personalize. I want to know if building the recommendation engine is possible without providing any data at first and then stream real time data from my app later with the putevents, putItem and putUser api from aws-sdk? I'm quite new to this so I'm quite confused with this initial step

kibakoriasu
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I want to know if building the recommendation engine is possible without providing any data at first and then stream real time data from my app later with the putevents, putItem and putUser api from aws-sdk?

Yes, it is possible. You just need to adjust the sequence of creating resources.

Interaction data is required for all Personalize recipes before a recommender can be created that provides recommendations. However, if you don't have interaction data (or enough data; see quotas and limits) to start with, you can create a dataset group and an interactions dataset, feed interactions to the dataset using the PutEvents API (see recording events page), and then create a domain recommender or custom solution when enough data has been ingested.

The minimum amount of interaction data (and potentially item metadata) required before you can train a model/recommender depends on the recipe that you select. Generally speaking, you will need 1000 interactions across 25 distinct users where each of those users has 2+ interactions. The domain recommenders also require specific event types. Check the docs linked above. The quality and relevance of recommendations will improve as you collect more data and retrain.

James J
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