NFT primary sale price and secondary sale prediction via deep learning

Name: NFT primary sale price and secondary sale prediction via deep learning
Author: Seyhan, Betül, Sefer, Emre

İsim	NFT primary sale price and secondary sale prediction via deep learning
Yazar	Seyhan, Betül, Sefer, Emre
Basım Tarihi:	2023-11-27
Basım Yeri	- Association for Computing Machinery, Inc
Konu	BERT, Blockchain, Deep learning, NFTs
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-840070240-2
Kayıt Numarası	6d2ae3f5-f4f8-4262-8795-ffe3cf73939d
Lokasyon	Computer Science
Tarih	2023-11-27
Örnek Metin	Non Fungible Tokens (NFTs) are blockchain-based unique digital assets defining ownership deeds. They can characterize various different objects such as collectible, art, and in-game items. In general, NFTs are encoded by blockchains smart contracts, and they are traded via cryptocurrencies. Their price and investors attention on them has remarkably increased especially in 2021, making them promising alternative class of investment. Surprisingly, predicting their prices has only recently started to be analyzed systematically. In this paper, we focus on predicting NFT primary sale price and secondary sale via deep learning. We use multimodal data, NFT images and NFT text characteristics when predicting their prices. Here, we show that contrasting the different and similar (DS) hierarchical features of images and text serves as an important identifying marker for their price, with the consequence that we only need to direct our attention to this aspect when designing a multimodal NFT price predictor. When designing NFT price predictor from multimodal data without using any financial attributes, we come up with Fine-Grained Differences-Similarities Enhancement Network (FG-DSEN), which improves detection with a simple and interpretable structure to enhance the DS aspect between images and text. According to detailed assessment on publicly available NFT dataset, our proposed approach outperforms baselines on both price direction prediction and secondary sale participation prediction according to several machine learning classification metrics.
DOI	10.1145/3604237.3626896

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

NFT primary sale price and secondary sale prediction via deep learning

Yazar Seyhan, Betül, Sefer, Emre

Basım Tarihi 2023-11-27

Basım Yeri - Association for Computing Machinery, Inc

Konu BERT, Blockchain, Deep learning, NFTs

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-840070240-2

Kayıt Numarası 6d2ae3f5-f4f8-4262-8795-ffe3cf73939d

Lokasyon Computer Science

Tarih 2023-11-27

Örnek Metin Non Fungible Tokens (NFTs) are blockchain-based unique digital assets defining ownership deeds. They can characterize various different objects such as collectible, art, and in-game items. In general, NFTs are encoded by blockchains smart contracts, and they are traded via cryptocurrencies. Their price and investors attention on them has remarkably increased especially in 2021, making them promising alternative class of investment. Surprisingly, predicting their prices has only recently started to be analyzed systematically. In this paper, we focus on predicting NFT primary sale price and secondary sale via deep learning. We use multimodal data, NFT images and NFT text characteristics when predicting their prices. Here, we show that contrasting the different and similar (DS) hierarchical features of images and text serves as an important identifying marker for their price, with the consequence that we only need to direct our attention to this aspect when designing a multimodal NFT price predictor. When designing NFT price predictor from multimodal data without using any financial attributes, we come up with Fine-Grained Differences-Similarities Enhancement Network (FG-DSEN), which improves detection with a simple and interpretable structure to enhance the DS aspect between images and text. According to detailed assessment on publicly available NFT dataset, our proposed approach outperforms baselines on both price direction prediction and secondary sale participation prediction according to several machine learning classification metrics.

DOI 10.1145/3604237.3626896