We just published our second research paper, built on Glasp data.
When we started
@_Glasp, the dream was simple: if we understand what each person highlights, we can personalize their entire reading experience.
This paper is us testing that dream rigorously. The answer surprised us.
Your reading history does say a lot about WHICH articles are yours. With a clean, leakage-free test, we could identify a person's documents among their co-readers' choices, even when the topics matched.
But WITHIN a document? Personalization stopped working. A model that knew your entire highlight history could not beat the shared, impersonal sense of what matters. Even frontier LLMs lost to a simple lead baseline at predicting highlights.
Our conclusion: personalization lives at the selection layer, not the salience layer. People differ in what they read. What stands out is mostly shared.
And honestly, my favorite part: we found a bias in our own evaluation that inflated our first result, audited it, and published the corrected number instead. That is the kind of research we want to do.
So maybe the future is not personalizing each reader harder. It is aggregating readers, turning shared salience into collective intelligence.
Co-authored with my co-founder
@KeiWatanabe17.
#Glasp #Research #Personalization #ReadingTech