Recommendation Systems (12): Large Language Models and Recommendation

Sat, 03 Jan 2026 09:00:00 +0000

A user opens a movie app and types: “Something like Inception, but less depressing.” A traditional recommender — collaborative filtering, two-tower DNN, even DIN — sees zero useful tokens here. It has no like button to count, no co-watch graph to traverse, no user ID with history. The query has to be turned into IDs before the system can do anything.

A Large Language Model has the opposite problem: it has too much world knowledge but doesn’t know who this user is. It knows Inception is a Christopher Nolan film with non-linear narrative and a hopeful-but-ambiguous ending; it knows what “depressing” means in cinema; it can name twenty films that fit. But it can’t tell you which of those twenty the current user has already seen, rated badly, or left half-watched.

Essence of Linear Algebra (18): Frontiers and Summary

Wed, 30 Apr 2025 09:00:00 +0000

We have walked the long road of linear algebra together. We started with arrows in the plane and ended at the gates of quantum computers, the inner workings of large language models, and the topology of data clouds. The remarkable thing — the thing this series has tried to make visible — is that the same handful of ideas keeps coming back. A vector is a state. A matrix is a transformation. A decomposition is the structure hiding inside the transformation. A norm tells you when you can trust your computation. Once you internalise that loop, every “frontier” looks less like a foreign country and more like another dialect of a language you already speak.

Large Language Models on Chen Kai Blog

Recommendation Systems (12): Large Language Models and Recommendation

Essence of Linear Algebra (18): Frontiers and Summary