A recently published scientific paper on the topic “Distributed Recommendation Systems” by an international team of researchers concludes that a lot of personal data is needed in order to make better recommendations. Our team of area Social-Computing replicated the study and subsequently refuted the result.
We encounter recommendation systems everywhere. Products, services and music are recommended to us on a daily basis. Traditionally, systems are organized in a centralized manner. Meaning sensitive data and models of service providers are administrated at external data processing centers. Data stored on our smartphone, e.g. ends up at data processing centers via third-party providers posing risks of user privacy violation.
By means of “distributed recommendation systems” data remains stored directly on the end device (e.g. smartphone, laptop, smartwatch) and does not disappear from there. User interaction data also remain in place implicating far better user privacy protection.
The research work was carried out within the scope of the COMET module DDAI and the EU-H2020 project TRUSTS. Thematically both of them outline the secure and explainable use of sensitive data. The resulting paper “Robustness of Meta Matrix Factorization Against Strict Privacy Constraints” was presented at ECIR 2021 during the “Federated Learning” session and is available free of charge.