Vectorized User Profiles: Personalization Without Prying

The Privacy Paradox

Users want software that "knows" them, but they don't want to be "tracked."

Traditional personalization relies on massive relational tables storing every click and purchase. This is a honeypot for hackers and a nightmare for GDPR compliance.

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The Semantic Profile Pattern

We are shifting clients toward On-Device or Ephemeral User Embeddings.

Instead of storing "The user bought a red hammer on Tuesday," we convert the user's recent behavior into a single vector (the "Intent Vector").

How it works:

1. Local Context: The user's browser keeps a short-term history of their clicks.
2. Edge Embedding: A small model (like Bert) creates a vector of their current interest.
3. Anonymized Query: The app sends the vector (not the user ID) to the server.
4. Recommendation: The server performs a vector search against the product catalog and returns relevant items.

// Client-side logic
const userIntentVector = await localModel.embed(recentActions);
const recommendations = await api.getSimilarProducts(userIntentVector); 
// Server never sees WHO the user is, only WHAT they want right now.

The Benefits

• Zero PII: The server never stores a permanent history of the user's specific actions.
• Instant Adaptation: If the user's interest shifts from "Work Boots" to "Running Shoes," the vector updates instantly.
• Compliance: You are not storing personal data; you are processing mathematical abstractions.

Personalization is a math problem, not a surveillance problem.