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 s...

Source: [HackerNoon](https://hackernoon.com/moving-from-matrix-factorization-to-transformer-based-recommendation-systems?source=rss)