Is Amazon Personalize Suitable for Infinite Feed Recommendation in Social Media App?

We are developing a social media app with an exploring feed, comparable to Instagram, TikTok or Reddit. Currently, our post recommendation algorithm is based on SQL functions to recommend posts based on the user's recent weighted interactions (views, likes, comments, etc.) with posts, taking into account the post tags. The post recommendations are paginated and filtered to exclude posts that the user has already seen. Although this solution provides good results, it is not scalable. Therefore, we are considering migrating to a machine learning-based recommendation engine once we have reached a critical mass of items and interaction data.

As a small team with limited ML experience but substantial experience with Amazon services in our application, we are considering using Amazon Personalize as our recommendation engine. However, I have come across discussions in forums and on Stack Overflow regarding the limitations of the service in the context of infinite feeds, particularly in terms of pagination and filtering of seen posts.

Therefore, I would like to pose the question to the community for discussion: Is Amazon Personalize suitable for such a use case, and what considerations should be taken into account when implementing it?