Assume a short video recommendation system with a million short videos and users. The users can watch & like videos.
Using a receipt such as USER_PERSONALIZATION (Users, Video as items, and Watched/Liked as interactions) the model will include thousands of interactions per user. To filter out the watched videos before recommending videos to the user, we can use Amazon Filter with:
EXCLUDE ItemID WHERE Interactions.EVENT_TYPE IN ("watched"). However according to https://docs.aws.amazon.com/personalize/latest/dg/frequently-asked-questions.html
"...when filtering, Amazon Personalize considers up to 200 historical interactions for a user, and up to 100 streamed interactions".
Therefore, for such use case, we might still recommend watched videos, as there will be more than 200 watched videos per user and the filter only considers up to 200 watched video (interactions).
Does it mean that Amazon Personalize is not considered a suitable solution for short video apps?
