Stream-based explainable recommendations via blockchain profiling

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Leal, F (Author) Other; Veloso, B (Author) FEP; Malheiro, B (Author) Other; Burguillo, JC (Author) Other; Chis, AE (Author) Other; Gonzalez Velez, H (Author) Other

Journal

Title: Integrated Computer-Aided Engineering

Vol. 29

Pages: 105-121

ISSN: 1069-2509

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00V-WS3

DOI: 10.3233/ica-210668

Abstract (EN): Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters - Memory-based and Model-based - using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17