Token-Curated Registry with Citation Graph

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). 11 For this property, PPR is often referred to as the random walk with restart (RWR) algorithm. 12 See the textbook [8], for more comprehensive review on peer-prediction method and other information elicitation models. 13 Uninformative equilibria are designated as a blind agreements in original DG13 [6].

Reputable List Curation from Decentralized Voting

“…Asgaonkar and Krishnamachari [2] also consider the payoffs inherent in a TCR and the behavior of a rational potential challenger. Finally, Ito and Tanaka [18] propose incorporating curator reputation into the TCR to determine the reward that individual curators receive.…”

Introduction of the Tokyo Waterfront Area Exhibition Room “TOKYO minatorie”

“…Despite its simplicity, this mechanism achieves the solution concept of strongly truthfulness [56] where peers maximize their expected rewards by constantly reporting accurate signals or constantly reporting wrong signals. 21 For task allocation and reward computation, mechanisms on information elicitation typically assume the presence of centralized authorities (Figure 3); however, several studies [97], [106]- [109] have attempted to apply these mechanisms to decentralized blockchain-related products. This includes efforts to integrate information elicitation with information aggregation, aiming to derive consensus from elicited information [110].…”