Paper Skygest: Personalized Academic Recommendations on Bluesky

TL;DR

Paper Skygest is a personalized academic content feed on Bluesky that enhances user engagement, is sustainably deployable, and offers a new way to study and experiment with recommender systems in decentralized social media.

Contribution

It introduces the first large-scale, continuously deployed personalized academic feed on a decentralized platform, with open code and architecture for academic use.

Findings

Sustained user engagement and satisfaction with Paper Skygest.

Increased research-related interactions among users.

Changes in interaction rates based on post order.

Abstract

We build, deploy, and evaluate Paper Skygest, a custom personalized social feed for scientific content posted by a user's network on Bluesky and the AT Protocol. We leverage a new capability on emerging decentralized social media platforms: the ability for anyone to build and deploy feeds for other users, to use just as they would a native platform-built feed. To our knowledge, Paper Skygest is the first and largest such continuously deployed personalized social media feed by academics, with over 50,000 weekly uses by over 1,000 daily active users, all organically acquired. First, we quantitatively and qualitatively evaluate Paper Skygest usage, showing that it has sustained usage and satisfies users; we further show adoption of Paper Skygest increases a user's interactions with posts about research, and how interaction rates change as a function of post order. Second, we share our full code and describe our system architecture, to support other academics in building and deploying such feeds sustainably. Third, we overview the potential of custom feeds such as Paper Skygest for studying algorithm designs, building for user agency, and running recommender system experiments with organic users without partnering with a centralized platform.