  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).
