Trust and Reputation Smart Contracts for Explainable Recommendations

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Leal, F (Author) Other; Veloso, B (Author) FEP; Malheiro, B (Author) Other; Vélez, HG (Author) Other

Conference proceedings International

Title: WorldCIST (1)

Pages: 124-133

8th World Conference on Information Systems and Technologies, WorldCIST 2020

7 April 2020 through 10 April 2020

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00S-2YC

DOI: 10.1007/978-3-030-45688-7_13

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

Language: English

Type (Professor's evaluation): Scientific