Trust and Reputation Smart Contracts for Explainable Recommendations

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1159)


Recommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency.


Smart contracts Trust and reputation Explainable recommendations Transparency Authenticity Traceability 



This work was partially financed by: (i) the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project \(\ll \)POCI-01-0145-FEDER-006961\(\gg \), and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2019; and (ii) the Irish Research Council in the framework of the EU ERA-NET CHIST-ERA project SPuMoNI: Smart Pharmaceutical Manufacturing (


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CCC/NCI – Cloud Competency CentreNational College of IrelandDublinIreland
  2. 2.INESC TECPortoPortugal
  3. 3.ISEP/IPP – School of EngineeringPolytechnic Institute of PortoPortoPortugal

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