Jo Seonbin, Jung Woo-Sung, Kim Hyunuk
Department of Physics, Pohang University of Science and Technology, Pohang, 37673, Korea.
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, 37673, Korea.
Sci Rep. 2024 Nov 16;14(1):28335. doi: 10.1038/s41598-024-78379-2.
Non-fungible tokens (NFTs), which are immutable and transferable tokens on blockchain networks, have been used to certify the ownership of digital images often grouped in collections. Depending on individual interests, wallets explore and purchase NFTs in one or more image collections. Among many potential factors of shaping purchase trajectories, this paper specifically examines how visual similarities between collections affect wallets' explorations. Our model characterizes each wallet's explorations with a Lévy flight and shows that wallets tend to favor collections having similar visual features to their previous purchases while their behaviors vary widely. The model also predicts the extent to which the next collection is close to the most recent collection of purchases with respect to visual features. These results are expected to enhance and support recommendation systems for the NFT market.
非同质化代币(NFT)是区块链网络上不可变且可转移的代币,已被用于认证通常以集合形式分组的数字图像的所有权。根据个人兴趣,钱包会在一个或多个图像集合中探索并购买NFT。在塑造购买轨迹的众多潜在因素中,本文特别研究了集合之间的视觉相似性如何影响钱包的探索行为。我们的模型用 Lévy 飞行来表征每个钱包的探索行为,并表明钱包倾向于青睐与其先前购买的视觉特征相似的集合,同时它们的行为差异很大。该模型还预测了下一个集合在视觉特征方面与最近购买集合的接近程度。这些结果有望增强并支持NFT市场的推荐系统。