DARSAN: A Decentralized Review System Suitable for NFT Marketplaces

TL;DR

DARSAN is a decentralized review system tailored for NFT marketplaces that incentivizes honest reviews and improves expert quality over time through a two-phase process and performance-based selection.

Contribution

The paper introduces DARSAN, a novel decentralized review framework for NFTs that enhances review reliability and expert selection without external oversight.

Findings

DARSAN favors honest reviewers in simulations.

The system improves expert pool quality over time.

DARSAN operates effectively even with malicious participants.

Abstract

We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.