Enhancing Voter Engagement in Decentralized Autonomous Organizations Through Recommender Systems

Authors

  • David Davó Complutense University of Madrid, Barcelona Supercomputing Center, Spain
  • Javier Arroyo Complutense University of Madrid, University of Alcalá, Spain

DOI:

https://doi.org/10.4114/intartif.vol29iss78pp103-122

Keywords:

recommender systems, online voting, decentralized autonomous organizations (DAOs), online communities, graph neural networks

Abstract

Decentralized Autonomous Organizations (DAOs) offer a novel approach to collectively governing projects through a democratic mechanism facilitated by blockchain. DAOs allow members to put forward and vote on proposals, thereby shaping the organization’s future. However, low voter turnout is common in DAO decision-making, particularly in large and active DAOs, where the high volume of proposals makes it unrealistic to expect members to track all proposals. Abstentionism threatens the voting system's effectiveness and the legitimacy of the results. We consider that recommender systems can help boost voter engagement. This article details the design of a recommendation approach tailored to aid DAO members in identifying proposals of interest, alongside its implementation and evaluation. The design accommodates the domain constraints that render off-the-shelf recommendation approaches inadequate, namely that proposals are short-lived and can only be recommended while available for voting. To the best of our knowledge, this is the first study to examine recommendation in DAO governance. To carry out our research, we have compiled a dataset, made publicly available, covering 12 of the most active DAOs. We compare a baseline, specifically designed to accommodate DAO-specific constraints, against a range of recommendation techniques. The findings confirm that personalized recommenders can often anticipate voting preferences, significantly outperforming the baseline. In turn, given the limitations of offline evaluation, an online evaluation using A/B testing would also be needed to fully assess their impact on participation. We also discuss how their implementation must carefully incorporate fairness and transparency to ensure community trust and adoption. We believe that proposal recommender systems in DAOs not only could drive engagement improvements similar to those observed in other online collaborative projects, but can also provide insights for collective governance settings beyond blockchain, such as cooperative organizations or participatory budgeting platforms.

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Author Biography

David Davó, Complutense University of Madrid, Barcelona Supercomputing Center, Spain

Former Research Assistant at the Complutense University of Madrid.

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Published

2026-07-28

How to Cite

Davó, D., & Arroyo, J. (2026). Enhancing Voter Engagement in Decentralized Autonomous Organizations Through Recommender Systems. Inteligencia Artificial, 29(78), 103–122. https://doi.org/10.4114/intartif.vol29iss78pp103-122

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