Networked Collaborative Recommendation Architecture

For any individual user, the amount of available content is exploding. Recommendations have become an integral part of digital business, helping people to find content and services, while at the same time enabling carefully targeted advertising.
A major challenge in recommendation systems is that they are either domain specific or need a substantial amount of data. This favours global data-driven platforms, available from few organisations, notalbly the GAFA group (Google, Amazon, Facebook and Apple).
The technology presented in this paper enables a recommendation engine network, in which all parties own and are able to administer their own data, challenging centralized models of today. The approach is based on exchangeable anonymous tokens. This enables a de-centralized recommendation architecture in which different recommendation engines can be located at the edges of networks and linked together, while respecting the ownership of data.
This paper introduces architectural models for the technology and a conceptual view of an ecosystem based on them.