Moving from Matrix Factorization to Transformer-Based Recommendation Systems

Personalization used to be a spreadsheet trick — factorize a user–item matrix, assume people who behaved alike would keep behaving alike, ship it. That's running out of road. Today's systems care about meaning (content embeddings), react inside a single session instead of a stale profile, and serve through a three-step loop: retrieve, rank, re-rank. The twist? Chase engagement too hard and you build a filter bubble that kills retention — so part of the job is breaking the loop on purpose.