A Recommendation Mechanism for a Non-Fungible Token Trading Platform
DOI:
https://doi.org/10.52731/liir.v003.055Abstract
Technology has evolved at an increasing pace over the decades, blockchain is used in many fields, and Non-Fungible Token (NFT) is one of its applications. With NFT, all digital assets can be freely traded because NFT establishes a unique token for each asset. From the transaction statistics, it can be found that the transaction volume in the past six months has increased explosively, and the projects uploaded to the NFT trading platform have also increased; but it also means customers need to spend more time finding what they want. Active recommendation systems can be used to solve this problem to reduce search cost. This study proposes a recommendation system for NFT tokens, compares the similarity between items, and analyzes buyers' collection data to prove that the recommendation theory proposed in this study is valid.
References
Nakamoto, Satoshi. “Bitcoin: A peer-to-peer electronic cash system.” Decentralized
Business Review (2008): 21260.
Wood, Gavin. “Ethereum: A secure decentralised generalised transaction ledger.”
Ethereum project yellow paper 151.2014 (2014): 1-32.
Huh, Seyoung, Sangrae Cho, and Soohyung Kim. “Managing IoT devices using
blockchain platform.” 2017 19th international conference on advanced communication
technology (ICACT). IEEE, 2017
ANTE, Lennart. “Smart contracts on the blockchain–A bibliometric analysis and review.
Telematics and Informatics, 2021, 57: 101519.
WANG, Qin, et al. “Non-fungible token (NFT): Overview, evaluation, opportunities and
challenges”. arXiv preprint arXiv:2105.07447, 2021.
FAIRFIELD, Joshua AT. “Tokenized: The law of non-fungible tokens and unique digital
property”. Ind. LJ, 2022, 97: 1261.
SGHAIER OMAR, A.; BASIR, Otman. “Capability-based non-fungible tokens approach
for a decentralized AAA framework in IoT”. In: Blockchain Cybersecurity, Trust and
Privacy. Springer, Cham, 2020. p. 7-31.
Dune Analytics, Artificial Intelligence Blog; https://dune.xyz/home.
LU, Jie, et al. “Recommender system application developments: a survey”. Decision
Support Systems, 2015, 74: 12-32.
LINDEN, Greg; SMITH, Brent; YORK, Jeremy. “Amazon. com recommendations:
Item-to-item collaborative filtering”. IEEE Internet computing, 2003, 7.1: 76-80.
PONNAM, Lakshmi Tharun, et al. “Movie recommender system using item based
collaborative filtering technique”. In: 2016 International Conference on Emerging Trends
in Engineering, Technology and Science (ICETETS). IEEE, 2016. p. 1-5.
CHEN, Rui, et al. “A survey of collaborative filtering-based recommender systems:
From traditional methods to hybrid methods based on social networks”. IEEE Access,
, 6: 64301-64320.
SHARMA, Lalita; GERA, Anju. “A survey of recommendation system: Research
challenges”. International Journal of Engineering Trends and Technology (IJETT), 2013,
5: 1989-1992.
DOWLING, Michael. “Is non-fungible token pricing driven by cryptocurrencies?”.
Finance Research Letters, 2022, 44: 102097.
ANTE, Lennart. “The non-fungible token (NFT) market and its relationship with Bitcoin
and Ethereum”. FinTech, 2022, 1.3: 216-224.
EDMUNDS, Angela; MORRIS, Anne. “The problem of information overload in
business organisations: a review of the literature”. International journal of information
management, 2000, 20.1: 17-28.
HÄUBL, Gerald; TRIFTS, Valerie. “Consumer decision making in online shopping
environments: The effects of interactive decision aids”. Marketing science, 2000, 19.1: 4-
RICCI, Francesco, ROKACH, Lior SHAPIRA, Bracha. “Introduction to recommender
systems handbook”. In: Recommender systems handbook. Springer, Boston, MA, 2011.
p. 1-35.
RASHID, Al Mamunur, et al. “Getting to know you: learning new user preferences in
recommender systems”. In: Proceedings of the 7th international conference on Intelligent
user interfaces. 2002. p. 127-134.
SCHAFER, J. Ben, et al. “Collaborative filtering recommender systems”. In: The
adaptive web. Springer, Berlin, Heidelberg, 2007. p. 291-324.
LOPS, Pasquale, et al. “Trends in content-based recommendation”. User Modeling and
User-Adapted Interaction, 2019, 29.2: 239-249.
SARWAR, Badrul, et al. “Item-based collaborative filtering recommendation
algorithms”. In: Proceedings of the 10th international conference on World Wide Web.
p. 285-295.
HERLOCKER, Jonathan L.; KONSTAN, Joseph A.; RIEDL, John. “Explaining
collaborative filtering recommendations”. In: Proceedings of the 2000 ACM conference
on Computer supported cooperative work. 2000. p. 241-250.
GEETHA, G., et al. “A hybrid approach using collaborative filtering and content based
filtering for recommender system”. In: Journal of Physics: Conference Series. IOP
Publishing, 2018. p. 012101.