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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhatia, G.K., Kumaraguru, P., Dubey, A., Buduru, A.B., Kaulgud, V.: WorkerRep:building trust on crowdsourcing platform using blockchain. Ph.D. thesis, IIIT-Delhi (2018)
Buccafurri, F., Lax, G., Nicolazzo, S., Nocera, A.: Tweetchain: an alternative to blockchain for crowd-based applications. In: International Conference on Web Engineering, pp. 386–393, Springer, Cham (2017)
Dignum, V.: Responsible artificial intelligence: designing AI for human values. ITU journal: ICT Discoveries 1(1), 1–8 (2017)
Fernández-Caramés, T.M., Fraga-Lamas, P.: Design of a fog computing, blockchain and IoT-based continuous glucose monitoring system for crowdsourcing mhealth. In: Multidisciplinary Digital Publishing Institute Proceedings, p. 37 (2018)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, chap. 3, pp. 77–118. Springer, Boston (2015), ISBN 978-1-4899-7637-6
Leal, F., Malheiro, B., Burguillo, J.C.: Prediction and analysis of hotel ratings from crowd-sourced data. In: World Conference on Information Systems and Technologies, pp. 493–502. Springer, Cham (2017)
Leal, F., Malheiro, B., Burguillo, J.C.: Trust and reputation modelling for tourism recommendations supported by crowdsourcing. In: World Conference on Information Systems and Technologies, pp. 829–838. Springer, Cham (2018)
Leal, F., Malheiro, B., Burguillo, J.C.: Incremental hotel recommendation with inter-guest trust and similarity post-filtering. In: World Conference on Information Systems and Technologies, pp. 262–272. Springer, Cham (2019)
Li, M., Weng, J., Yang, A., Lu, W., Zhang, Y., Hou, L., Liu, J.N., Xiang, Y., Deng, R.H.: Crowdbc: A blockchain-based decentralized framework for crowdsourcing. IEEE Trans. Parallel Distrib. Syst. 30(6), 1251–1266 (2018)
Lu, Y., Tang, Q., Wang, G.: Zebralancer: private and anonymous crowdsourcing system atop open blockchain. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 853–865. IEEE (2018)
Nam, K., Dutt, C.S., Chathoth, P., Khan, M.S.: Blockchain technology for smart city and smart tourism: latest trends and challenges. Asia Pac. J. Tour. Res., 1–15 (2019)
Nofer, M., Gomber, P., Hinz, O., Schiereck, D.: Blockchain. Bus. Inf. Syst. Eng. 59(3), 183–187 (2017)
Önder, I., Treiblmaier, H., et al.: Blockchain and tourism: three research propositions. Ann. Tour. Res. 72(C), 180–182 (2018)
Pilkington, M.: 11 blockchain technology: principles and applications. Research Handbook on Digital Transformations, vol. 225 (2016)
Rejeb, A., Rejeb, K.: Blockchain technology in tourism: applications and possibilities. World Sci. News 137, 119–144 (2019)
Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Recommender systems handbook, pp. 479–510. Springer, Cham (2011)
Veloso, B., Leal, F., Malheiro, B., Moreira, F.: Distributed trust & reputation models using blockchain technologies for tourism crowdsourcing platforms. Procedia Comput. Sci. 160, 457–460 (2019)
Acknowledgements
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 (http://www.spumoni.eu).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Leal, F., Veloso, B., Malheiro, B., González-Vélez, H. (2020). Trust and Reputation Smart Contracts for Explainable Recommendations. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-45688-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45687-0
Online ISBN: 978-3-030-45688-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)