Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Tzu-Yu Yeh, R. Kashef
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引用次数: 17

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.
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基于信任的区块链协同过滤推荐系统
区块链是所有加密货币交易的数字化、去中心化公共账本。区块链正在通过实现创新的商业实践来改变行业。它的革命性力量已经渗透到银行、金融、贸易、制造、供应链管理、医疗保健和政府等领域。区块链和物联网(BIOT)将区块链时代应用于物联网通信系统,因此,安全和隐私因素是可以实现的。区块链技术与物联网的融合创造了现代去中心化系统。BIOT模型可以应用于包括电子商务在内的各个行业,以促进去中心化、可扩展性和安全性。这项研究呼吁在区块链和推荐系统上进行创新和高级的重新搜索。我们的目标是通过在主区块链协议中添加智能合约,利用区块链支持的安全多方计算的优势,构建一个基于安全和信任的系统。将推荐系统和区块链技术相结合,可以使在线活动更加安全和私密。构建了一个系统,供企业协同创建一个安全的数据库,并使用智能合约系统托管一个稳定更新的模型。学习案例研究包括一个向用户推荐电影的模型。通过一种激励机制来评估模型的准确性,该机制提供了一个具有可接受性能的完全基于信任的推荐系统。
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