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I've explored tools like amazon personalize, etc. for generating recommendations. It seems like amazon personalize is appropriate when all the content is with the company/a single entity. For example, in Netflix, all the content (the catalogue of movies, tv shows, etc.) is with them and they generate personalized movie/tv show recommendations.

But what if there's a platform similar to Youtube, TikTok, where users can:

  • post content (users are continuously generating content)
  • view other users content and interact (like, share, repost, comment)
  • follow other users

When there is user-generated content like this and users follow other users (meaning they probably want recommendations from users they follow), how do we give recommendations? What algorithms and tools can be used?

Lots of content - handling the cold start problem

And when there is user-generated content, there is going to be lots of content being generated every minute. So, how do we handle the cold start problem (i.e. how do we decide who to recommend all of this new influx of content too)? Usually, we might experiment with this new content, like recommend it to some users, see how they're responding and appropriately decide how to recommend this content. But when there is a very high frequency of content being created, how do we reduce the amount of time it takes to give recommendations/push the new content to users quickly?

And does anybody know if the questions mentioned above can be addressed using Amazon Personalize itself (to some extent maybe)?

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