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Title: Trust and reputation smart contracts for explainable recommendations
Authors: Leal, Fátima
Veloso, Bruno
Malheiro, Benedita
González-Vélez, Horacio
Keywords: Smart contracts
Trust and reputation
Explainable recommendations
Issue Date: 18-May-2020
Publisher: Springer
Citation: Leal, F., Veloso, B., Malheiro, B., & González-Vélez, H. (2020). Trust and reputation smart contracts for explainable recommendations. In A. Rocha, H. Adeli, L. Reis, S. Costanzo, I. Orovic, & F. Moreira (Eds.), Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol. 1159 (pp. 124-133). Springer. Repositório Institucional UPT.
Abstract: 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.
ISBN: 978-3-030-45687-0 (Print)
978-3-030-45688-7 (Online)
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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