# Token-Curated Registry with Citation Graph

## TL;DR

This paper introduces CitedTCR, a token-curated registry that leverages citation graphs and expert curators to improve the curation of academic and technical content, combining network analysis and token incentives.

## Contribution

It proposes a novel method integrating citation relationships and token incentives for expert curation in a decentralized registry, addressing a gap in technical content curation.

## Key findings

- CitedTCR effectively incentivizes registration and curation.
- The system successfully develops an evolving citation graph.
- Experimental results confirm the model's incentive compatibility.

## Abstract

In this study, we aim to incorporate the expertise of anonymous curators into a token-curated registry (TCR), a decentralized recommender system for collecting a list of high-quality content. This registry is important, because previous studies on TCRs have not specifically focused on technical content, such as academic papers and patents, whose effective curation requires expertise in relevant fields. To measure expertise, curation in our model focuses on both the content and its citation relationships, for which curator assignment uses the Personalized PageRank (PPR) algorithm while reward computation uses a multi-task peer-prediction mechanism. Our proposed CitedTCR bridges the literature on network-based and token-based recommender systems and contributes to the autonomous development of an evolving citation graph for high-quality content. Moreover, we experimentally confirm the incentive for registration and curation in CitedTCR using the simplification of a one-to-one correspondence between users and content (nodes).