DIT: Dimension Reduction View on Optimal NFT Rarity Meters

TL;DR

This paper introduces a novel dimension reduction approach to design and evaluate NFT rarity meters, proposing the DIT measure and demonstrating its superior performance over existing methods using the ROAR benchmark.

Contribution

It develops an optimal rarity meter using non-metric multidimensional scaling and introduces DIT as a new performance measure for NFT rarity evaluation.

Findings

DIT outperforms existing rarity meters in evaluations.

New performance measures improve NFT rarity assessment.

Dimension reduction techniques enhance the design of rarity meters.

Abstract

Non-fungible tokens (NFTs) have become a significant digital asset class, each uniquely representing virtual entities such as artworks. These tokens are stored in collections within smart contracts and are actively traded across platforms on Ethereum, Bitcoin, and Solana blockchains. The value of NFTs is closely tied to their distinctive characteristics that define rarity, leading to a growing interest in quantifying rarity within both industry and academia. While there are existing rarity meters for assessing NFT rarity, comparing them can be challenging without direct access to the underlying collection data. The Rating over all Rarities (ROAR) benchmark addresses this challenge by providing a standardized framework for evaluating NFT rarity. This paper explores a dimension reduction approach to rarity design, introducing new performance measures and meters, and evaluates them using the ROAR benchmark. Our contributions to the rarity meter design issue include developing an optimal rarity meter design using non-metric weighted multidimensional scaling, introducing Dissimilarity in Trades (DIT) as a performance measure inspired by dimension reduction techniques, and unveiling the non-interpretable rarity meter DIT, which demonstrates superior performance compared to existing methods.