2023
doi: 10.12720/jait.14.3.518-522
Recommendation System with Content-Based Filtering in NFT Marketplace
Edi Surya Negara1,*
,
Sulaiman1 ,
Ria Andryani1 ,
Prihambodo Hendro Saksono1 ,
Yeni Widyanti2
- 1Data Science Interdisciplinary Research Center, Computer Science Faculty, Universitas Bina Darma, Palembang, Indonesia
- 2Economics and Business Faculty, Universitas Bina Darma, Palembang, Indonesia
- Manuscript receivedAugust 22, 2022
- revisedSeptember 29, 2022
- acceptedOctober 24, 2022
- publishedJune 7, 2023
Abstract
Non-Fungible Token (NFT) is a digital asset that cannot be exchanged or used, and uses Crypto currency values according to the type of digital money used, for example Bitcoin, Ethereum. The NFT Marketplace is a platform for buying and selling NFT like Tokopedia. This common problem is often encountered in e-commerce, especially in the NFT Marketplace, among other buyers often having difficulty finding products. This makes it difficult for the NFT Marketplace and sellers to promote products that match the preferences of potential buyers. A recommendation system that is very much needed in overcoming these problems, responding to these problems the author tries to make a recommendation system using the Content Based Filtering approach using the cosine similarity. The results of this study indicate that the Machine Learning model can provide Top-N recommendations from the product being sought.
Keywords
- crypto currency
- non-fungible token
- content-based filtering
- Non-Fungible Token (NFT) marketplace
How to Cite
Edi Surya Negara, Sulaiman, Ria Andryani, Prihambodo Hendro Saksono, and Yeni Widyanti, "Recommendation System with Content-Based Filtering in NFT Marketplace," Journal of Advances in Information Technology, vol. 14, no. 3, pp. 518-522, 2023. https://doi.org/10.12720/jait.14.3.518-522
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
