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Lisi, A., De Salve, A., Mori, P. and Ricci, L. (2019) A Smart Contract Based Recommender System. In: Djemame, K., Altmann, J., Bañares, J., Agmon Ben-Yehuda, O. and Naldi, M., Eds., Economics of Grids, Clouds, Systems, and Services, GECON 2019, Lecture Notes in Computer Science, Springer, Cham, 29-42.
https://doi.org/10.1007/978-3-030-36027-6_3

has been cited by the following article:

• TITLE: Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

  AUTHORS: Tzu-Yu Yeh, Rasha Kashef

  KEYWORDS: Blockchain, Recommendation Systems, Smart Contract, Predictions, Accuracy

  JOURNAL NAME: Advances in Internet of Things, Vol.10 No.4, October 13, 2020

  ABSTRACT: A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.