how do you solve cold-start for personalization when your app has no behavioral data yet?

Reddit r/AI_Agents News
cold-start personalization recommendation-systems content-recommendation user-onboarding machine-learning

Summary

A software engineer asks for strategies to bootstrap personalization for new users with no behavioral data, discussing the cold-start problem in content recommendation.

im a swe in a small startup building a content recommendation feature. the problem i keep running into is that we have zero behavioral signal on new users, so their first session is just generic top-of-funnel content. i can't ask users to rate 20 items on signup like netflix used to ,nobody does that anymore. sign-in-with-google gives me an email and a name, that's it. how are people bootstrapping personalization for new users in 2026? is everyone just eating the cold-start cost and waiting weeks for enough in-app data, or is there a smarter pattern i'm missing?
Original Article

Similar Articles

FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users

arXiv cs.CL

FSPO proposes a few-shot preference optimization algorithm for LLM personalization that reframes reward modeling as meta-learning, enabling models to quickly infer personalized reward functions from limited user preferences. The method achieves 87% personalization performance on synthetic users and 70% on real users through careful synthetic preference dataset construction.