You can keep recommendation embeddings fresh and personalized without expensive retraining cycles by storing preferences in a sparse tree structure and recomputing embeddings on-the-fly as new ratings arrive.
This paper solves the embedding staleness problem in recommendation systems by proposing mutable sketches—a method that updates user embeddings instantly as new ratings arrive, without retraining.