Decentralized Blockchain Network
Paper
Paper/Presentation Title | Decentralized Blockchain Network |
---|---|
Presentation Type | Paper |
Authors | Abduljabbar, Tamara Abdulmunim, Tao, Xiaohui, Zhang, Ji, Yong, Jianming and Zhou, Xujuan |
Journal or Proceedings Title | Proceedings of 2022 Tenth International Conference on Advanced Cloud and Big Data (CBD) |
Journal Citation | pp. 234-239 |
Number of Pages | 6 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | China |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CBD58033.2022.00049 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10024495 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10024507/proceeding |
Conference/Event | 2022 Tenth International Conference on Advanced Cloud and Big Data (CBD) |
Event Details | 2022 Tenth International Conference on Advanced Cloud and Big Data (CBD) Parent International Conference on Advanced Cloud and Big Data Delivery In person Event Date 04 to end of 05 Nov 2022 Event Location Guilin, China |
Abstract | Recommender systems strongly affect a company’s profitability and regularly play a significant role in the success of companies’ sales policies. Companies are now collecting vast amounts of user information to make sure they can use recommender systems and related algorithms for recommending products and services according to customer needs, preferences, and likenesses. Unfortunately, privacy concerns prevent users from sharing data generously with interested companies, even if the quality of the recommendations would be improved. One way to overcome privacy-related issues is using a secured solution, such as blockchain technologies for privacy-based applications. Integrating blockchain technology and the Internet of Things creates modern decentralized systems. The integration gives scalability and security to recommender systems. This research calls for innovative and advanced research on Blockchain and recommendation systems. We constructed a blockchain-based recommender system using “the Movielens” dataset. 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 recommendation system with acceptable performance. |
Keywords | Recommender systems; Blockchain; Privacypreserving; Decentralized system; Ethereum |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mathematics, Physics and Computing |
School of Business |
https://research.usq.edu.au/item/z58y3/decentralized-blockchain-network
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