2 posts tagged with "recommendations"

Vector search is eating the world. Embedding-based retrieval now powers product search at every major e-commerce platform, drives the retrieval layer of RAG systems, and sits at the core of most AI-powered search rewrites. But there is a category of user that these systems fail silently and consistently: the browsing user. Not because the embeddings are bad. Because they were built to solve a different problem.

The fundamental assumption behind semantic search is that users arrive with a query that approximates what they want. Optimize for proximity in embedding space to that query, and you win. But a significant fraction of real users arrive with something closer to curiosity than a query — and for them, the nearest neighbors in vector space are exactly the wrong answer.

You build a personalization feature, wire it into your app, and ship it. Week one arrives. The system dutifully serves every new user the same handful of globally popular items — your AI, supposedly intelligent, is no smarter than an alphabetically sorted list. Your engagement metrics barely move. Your team concludes the model needs more tuning. It doesn't. The model is working exactly as designed. The problem is you asked it to learn before it had anything to learn from.

This is the cold start problem, and it kills more AI features than bad models ever will.

The core dynamic is circular: a behavioral ML system needs user interactions to produce useful predictions, but it needs to produce useful predictions to earn user interactions. One large e-commerce platform documented that cold start affected more than 60% of their new users — and those users were receiving misfired recommendations that measurably hurt conversion rates. In aggregate metrics, this signal was nearly invisible because warm users masked the damage.