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.
References
[1]
Syed, T.A., Alzahrani, A., Jan, S., Siddiqui, M.S., Nadeem, A. and Alghamdi, T. (2019) A Comparative Analysis of Blockchain Architecture and Its Applications: Problems and Recommendations. IEEE Access, 7, 176838-176869.
https://doi.org/10.1109/ACCESS.2019.2957660
[2]
Frey, R.M., Vuckovac, D. and Ilic, A. (2016) A Secure Shopping Experience Based on Blockchain and Beacon Technology. In RecSys Posters.
[3]
Xue Tan and Kashef, R. (2019) Predicting the Closing Price of Cryptocurrencies: A Comparative Study. Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (DATA ‘19), New York, December 2019, 1-5. https://doi.org/10.1145/3368691.3368728
[4]
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
[5]
Fallis, A. (2013) Rootstock Platform: Bitcoin Powered Smart Contracts—White Paper. Journal of Chemical Information and Modeling, 53, 1689-1699.
https://doi.org/10.1021/ci400128m
[6]
Luu, L., Chu, D.-H., Olickel, H., Saxena, P. and Hobor, A. (2016) Making Smart Contracts Smarter. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 24-28 October 2016, 254-269.
https://doi.org/10.1145/2976749.2978309
[7]
Ricci, F., Rokach, L. and Shapira, B. (2011) Introduction to Recommender Systems Handbook. Springer, Berlin. https://doi.org/10.1007/978-0-387-85820-3
[8]
Sree Lakshmi, S. and Adi Lakshmi, T. (2014) Recommendation Systems: Issues and Challenges. International Journal of Computer Science and Information Technologies, 5, 5771-5772.
[9]
Moreno, M.N., Segrera, S., Lopez, V.F., Muñoz, M.D. (2015) Web Mining Based Framework for Solving Usual Problems in Recommender Systems. A Case Study for Movies’ Recommendation. Elsevier, Amsterdam.
https://doi.org/10.1016/j.neucom.2014.10.097
[10]
Lu, J., Wu, D.S., Mao, M.S., Wang, W. and Zhang, G.Q. (2015) Recommender System Application Developments: A Survey. Decision Support Systems, 74, 12-32.
https://www.sciencedirect.com/science/article/pii/S0167923615000627
https://doi.org/10.1016/j.dss.2015.03.008
[11]
Alhijawi, B., Kilani, Y. and Alsarhan, A. (2020) Improving Recommendation Quality and Performance of Genetic-Based Recommender System. International Journal of Advanced Intelligence Paradigms, 15, 77-88.
https://doi.org/10.1504/IJAIP.2020.104108
[12]
Nilashi, M., bin Ibrahim, O., Ithnin, N. and Sarmin, N.H. (2015) A Multi-Criteria Collaborative Filtering Recommender System for the Tourism Domain Using Expectation Maximization (EM) and PCA-ANFIS. Electronic Commerce Research and Applications, 14, 542-562.
https://www.sciencedirect.com/science/article/pii/S1567422315000599
https://doi.org/10.1016/j.elerap.2015.08.004
[13]
Levinas, C.A. (2014) An Analysis of Memory Based Collaborative Filtering Recommender Systems with Improvement Proposals. MS Thesis, Universitat Politècnica de Catalunya, Barcelona.
[14]
Mansur, F., Patel, V. and Patel, M. (2017) A Review on Recommender Systems. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 17-18 March 2017, 1-6.
https://doi.org/10.1109/ICIIECS.2017.8276182
[15]
Guimarães, R., Rodríguez, D.Z., Rosa, R.L. and Bressan, G. (2016) Recommendation System Using Sentiment Analysis Considering the Polarity of the Adverb. 2016 IEEE International Symposium on Consumer Electronics (ISCE), Sao Paulo, 28-30 September 2016, 71-72. https://ieeexplore.ieee.org/abstract/document/7797377/
https://doi.org/10.1109/ISCE.2016.7797377
[16]
Chen, J.R., Zhao, C.X., Uliji and Chen, L.F. (2019) Collaborative Filtering Recommendation Algorithm Based on User Correlation and Evolutionary Clustering. Complex & Intelligent Systems, 6, 147-156. https://doi.org/10.1007/s40747-019-00123-5
[17]
Jain, A., Jain, V. and Kapoor, N. (2016) A Literature Survey on the Recommendation System Based on the Sentimental Analysis. Advanced Computational Intelligence, 3, 25-36. https://www.academia.edu/download/42598847/3116acii03.pdf
https://doi.org/10.5121/acii.2016.3103
Kashef, R. and Warraich, M. (2020) Homogeneous vs. Heterogeneous Distributed Data Clustering: A Taxonomy. In: Alhajj, R., Moshirpour, M. and Far, B., Eds., Data Management and Analysis. Studies in Big Data, Springer, Cham, 51-66.
https://doi.org/10.1007/978-3-030-32587-9_4
[20]
Yang, S., Korayem, M., AlJadda, K., Grainger, T. and Natarajan, S. (2017) Combining Content-Based and Collaborative Filtering for Job Recommendation System: A Cost-Sensitive Statistical Relational Learning Approach. Knowledge-Based Systems, 136, 37-45. https://www.sciencedirect.com/science/article/pii/S095070511730374X
https://doi.org/10.1016/j.knosys.2017.08.017
[21]
Sulthana, A.R. and Ramasamy, S. (2019) Ontology and Context-Based Recommendation System Using Neuro-Fuzzy Classification. Computers & Electrical Engineering, 74, 498-510.
https://www.sciencedirect.com/science/article/pii/S0045790617337382
https://doi.org/10.1016/j.compeleceng.2018.01.034
[22]
Zhang, H.-R., Min, F., He, X. and Xu, Y.-Y. (2015) A Hybrid Recommender System Based on User-Recommender Interaction. Mathematical Problems in Engineering, 2015, Article ID: 145636. https://doi.org/10.1155/2015/145636
[23]
Maesa, D.D.F., Mori, P. and Ricci, L. (2019) A Blockchain Based Approach for the Definition of Auditable Access Control Systems. Computers & Security, 84, 93-119.
https://doi.org/10.1016/j.cose.2019.03.016
[24]
Min, H. (2019) Blockchain Technology for Enhancing Supply Chain Resilience. Business Horizons, 62, 35-45.
https://www.sciencedirect.com/science/article/pii/S0007681318301472
https://doi.org/10.1016/j.bushor.2018.08.012
[25]
Kashef, R. and Niranjan, A. (2017) Handling Large-Scale Data Using Two-Tier Hierarchical Super-Peer P2P Network. Proceedings of the International Conference on Big Data and Internet of Thing, New York, 20-22 December 2017, 52-56.
https://doi.org/10.1145/3175684.3175726
[26]
Fanning, K. and Centers, D. (2016) Blockchain and Its Coming Impact on Financial Services. Journal of Corporate Accounting & Finance, 27, 53-57.
https://doi.org/10.1002/jcaf.22179
[27]
Harris, J.D. and Waggoner, B. (2019) Decentralized & Collaborative AI on Blockchain. 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, 14-17 July 2019, 368-375. https://doi.org/10.1109/Blockchain.2019.00057
[28]
Ibrahim, A., Kashef, R., Li, M., Valencia, E. and Huang, E. (2020) Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables. Journal of Risk and Financial Management, 13, 189. https://doi.org/10.3390/jrfm13090189
[29]
Behnke, K. and Janssen, M.F.W.H.A. (2020) Boundary Conditions for Traceability in Food Supply Chains Using Blockchain Technology. International Journal of Information Management, 52, Article ID: 101969.
https://www.sciencedirect.com/science/article/pii/S0268401219303536
https://doi.org/10.1016/j.ijinfomgt.2019.05.025
[30]
Corbet, S., Larkin, C., Lucey, B., Meegan, A. and Yarovaya, L. (2020) Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position. Journal of Financial Stability, 46, Article ID: 100706. https://doi.org/10.1016/j.jfs.2019.100706
[31]
Kashef, R. and Kamel, M.S. (2008) Distributed Peer-to-Peer Cooperative Partitional-Divisive Clustering for gene expression datasets. 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Sun Valley, 15-17 September 2008, 143-150. https://doi.org/10.1109/CIBCB.2008.4675771
[32]
Crosby, M., Pattanayak, P., Verma, S. and Kalyanaraman, V. (2016) Blockchain Technology: Beyond Bit Coin. Applied Innovation, No. 2, 6-19.
https://j2-capital.com/wp-content/uploads/2017/11/AIR-2016-Blockchain.pdf
[33]
Shen, C. and Pena-Mora, F. (2018) Blockchain for Cities—A Systematic Literature Reviews. IEEE Access, 6, 76787-76819.
https://ieeexplore.ieee.org/abstract/document/8531608/
https://doi.org/10.1109/ACCESS.2018.2880744