Sam Liebeskind

The pattern is the same: the most sophisticated platforms are treating user behavior as sequences, not snapshots, recognizing that static embeddings miss the temporal dynamics and session context of how people actually use these products.

The difference becomes clear in how the feed responds to your behavior. Let’s say you click on a note about climate policy. In the old two-tower system, this single interaction would contribute minimally to your overall profile, especially if climate wasn’t already one of your core subscription topics. The model’s simplistic averaging approach... See more

The key difference is that sequential models maintain a dynamic representation of your current state. As you move through your feed, clicking on posts, subscribing to publications, engaging with notes, the model updates its understanding of where you are. It’s not just updating a long-term profile of your tastes. It’s tracking the momentum and... See more

The two-tower era: efficient but static

The two-tower architecture has been the workhorse of modern recommendation systems, and for good reason: it’s elegant, scalable, and remarkably effective at learning patterns from massive amounts of data. Here’s how it worked for us once we deployed it in early 2024.

The system had two neural networks working... See more

This is good. https://t.co/0fTmOZ1xux