Preference Accumulation
A model for building an accurate picture of a user from behaviour over time, without asking them to describe themselves.
People’s preferences are not stable facts waiting to be retrieved. They shift with context, evolve over time, and often only become clear through exposure to concrete options. Systems that ask users to declare what they want at the start of a relationship begin with the least reliable data they will ever have.
The profile builds from what users do. Signals are weighted by the commitment they required:
Declared preferences set an initial direction but are treated as a prior, overridden progressively as behaviour accumulates.
In Orion , this produced a distinction between two types of preference that update at different speeds:
Surface preferences (volume, price, distance, timing) update on a single confirmed signal. A user who sees three jazz results and redirects toward Something Quieter tells the system that the genre was alright, but the level of volume was wrong. The profile updates on volume immediately while keeping the genre intact.
Core preferences (genre, format, social mode) require the same signal to recur across sessions before the profile shifts. A user who rejects rock once may simply not be in the mood. When the user has rejected it across three sessions, the system updates that at the preference level.
The distinction between surface and core will manifest differently across domains, but the underlying principle remains.
The person produces a position they can state, defend, and claim as their own. What qualifies as that position varies by application: it could be a written argument, or a stated decision, or a reasoned recommendation. The end of a conversion is not as important a signal as the person’s ability to own the outcome of the conversation.
The person acts on recommendations with fewer redirections and over time, the ratio of redirections to direct actions shifts towards more confirmations.
Preference Accumulation is a relationship model, not a session model. Most systems optimise for the next interaction. This one optimises for accuracy across a relationship. The profile does not reset between sessions — each interaction either confirms or quietly revises what the system already holds.
Preference Accumulation is also a foundation model: it could be applied to every other model and a vast array of products, while not being a product of its own.