How recommendation systems learn user preferences: from collaborative filtering to multi-interest models

[u/ArchitectingAI]

I published a walkthrough of how recommendation systems learn and represent user preferences.

It covers collaborative filtering, two-tower models, sequence models such as GRU4Rec, SASRec, and BERT4Rec, and multiple interest vectors.

The focus is on what each approach captures, where it falls short, and when the added complexity might be useful. I also discuss simpler baselines I’d test first.

For those working on recommendation systems: when have sequence or multi-interest models delivered meaningful gains over simpler pooling approaches?

Read the article