Networked Collaborative Recommendation Architecture

    Research output: Contribution to conferenceConference articleScientificpeer-review


    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.
    Original languageEnglish
    Number of pages8
    Publication statusPublished - 2019
    MoE publication typeNot Eligible
    EventIBC2019 Conference - RAI, Amsterdam, Netherlands
    Duration: 13 Sept 201917 Sept 2019


    ConferenceIBC2019 Conference


    • collaborative filtering
    • privacy
    • upcv
    • networked systems


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