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I've used AWS personalize to create a campaign that can successfully produce recommendations for the users/events/items that I have uploaded.

I now want to produce recommendations for new users - ones not in the initial dataset. I thought the way to do this was to create an event stream, post their initial interactions, and then somehow this would get blended into the campaign, but I get the same recommendations back regardless of what I seed the new user with.

What's the correct way of achieving this?

Dan
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From what I understand from https://docs.aws.amazon.com/cli/latest/reference/personalize/create-solution-version.html and https://aws.amazon.com/personalize/pricing/ you have to retrain your model to incorporate new data with create-solution-version call. After the new version is created for an active solution campaign should start returning updated results.

Please let me know if this resolves your problem.

Radzikowski
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  • create-solution-version takes a long time to compute (and is also very expensive). This would work for updating recommendations overnight, but not for giving immediate recommendations to a new user – Dan Oct 13 '19 at 18:57
  • I don't think getting recommendations based on new data can work in any other way. For the recommendations for new users you may use HRNN-Coldstart recipe, but it won't be based on the data you provide for this user but rather general data from the solution version. – Radzikowski Oct 14 '19 at 04:53
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You can get default recommendations for any new user(USER_ID) as long as you don't have any filter created for user_schema. If you have created filter like gender etc, you will have to first call "put_user" API for new user and insert that record in AWS personalize "user dataset". Once, that put request is executed during the registration process or so, then your user will start getting recommendations. As that user interacts more, their recommendations will change in real time.

https://docs.aws.amazon.com/personalize/latest/dg/importing-users.html

Shashwat Gupta
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