Equilibrium-Based NFT Marketplace Recommendation for NFTs with Breeding

Abstract

Recently, Non-Fungible Tokens (NFTs) have attracted attention as valuable digital assets. However, NFT marketplaces face complex challenges in simultaneously recommending optimal pricing to sellers and desirable NFTs to buyers. Unlike conventional marketplaces that focus only on balancing demand and supply between sellers and buyers, these tasks are complicated by intricate value interdependencies arising from diverse buyer preferences, budgets, trait rarities, and the unprecedented breeding mechanisms. This paper formulates the NFT Project Pricing/Purchasing Recommendation (NP3R) problem, aiming to achieve a competitive equilibrium that concurrently optimizes seller revenue and buyer utility. We introduce BANTER, an iterative algorithm that jointly determines (1) optimal NFT purchases for buyers (via NFT-REC), considering breeding utility and current prices; and (2) optimal pricing for sellers (via PRICEREC), based on aggregated demand from NFT-REC. To efficiently manage the combinatorial complexity of breeding, we devise Optimal Parent Pair Selection (OPPS) and Heterogeneous Parent Set Selection (HPSS) schemes. Theoretical analysis guarantees BANTER to converge to a competitive equilibrium. Experiments on five real-world NFT datasets demonstrate its effectiveness in enhancing both seller revenue and average buyer utility. Source code: https://github.com/jimmy-academia/BANTER