Token-Curated Registry with Citation Graph

Kensuke Ito,Hideyuki Tanaka

doi:10.5195/ledger.2019.182

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).

Highlights

For many blockchain-based decentralized applications (DApps), one of the challenges is the reliability of information originating from an off-chain environment
This figure reveals that all correlation coefficients are within the range of 0.4 to 0.7, which can be regarded as moderately correlated. They begin to converge between 0.65 and 0.7 when n exceeds 10. These results indicates that CitedTCR can retain sufficient incentive to register high-quality content, especially when it assigns more than 10 curators to Gt, even though curator assignment relies on the Personalized PageRank (PPR) algorithm without base nodes
We proposed CitedTCR, which incorporates the expertise of anonymous curators into existing token-curated registry (TCR) by constructing a reliable citation graph, which is a common proxy for measuring the quality of technical content

Summary

Introduction

For many blockchain-based decentralized applications (DApps), one of the challenges is the reliability of information originating from an off-chain environment. In this study, we aim to incorporate the expertise of anonymous curators into TCRs using a protocol called CitedTCR, which leverages a citation graph for curator assignment and uses a peer-prediction mechanism to compute the number of reward tokens paid to the curators. Similar to the academic peer-review process, in which researchers who have produced highquality papers with a large number of citations are more likely to be selected as reviewers in their field of expertise Note that this form of curator assignment serves as an incentive for applicants to register high-quality content in CitedTCR because users may have more opportunities to obtain reward tokens as curators if their content in Gt attracts a large number of citations.[9,10] The citation graph serves as a proxy for the expertise of anonymous curators; the reliability of Gt from the perspective of both curation and registration is ensured.

Related Work

Experimental Studies

Conclusion

34 See Faltings’ and Radanivic’s Game Theory for Data Science