arXiv:2606.10398v1 Announce Type: cross Abstract: Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighter and a co-readership identity control (the same document highlighted by many users, which holds document and topic fixed and asks whether a person's own history predicts their marks better than another reader's does), we map the shape and limits of personalization across reading altitudes. At the document altitude we give the clean, leakage-free, identity-controlled measurement that prior next-document evaluations could only upper-bound: a perso
The proliferation of personalized content platforms and the increasing sophistication of AI algorithms make understanding personalization's true impact crucial now.
This research provides a foundational understanding of how personalization truly impacts user engagement, moving beyond mere salience to selection, which is critical for future AI agent design and social media platforms.
The explicit measurement of personalization's effect, separating it from topic salience, refines our understanding of how effective recommendation systems can be and where their limits lie.
- · Platforms optimizing for user engagement
- · AI agents specializing in content curation
- · Researchers in human-computer interaction
- · Advertising technology companies
- · Generic recommendation systems
- · Platforms over-relying on simple salience metrics
- · Content creators whose work isn't easily personalized
More effective and nuanced personalization algorithms will emerge, leading to higher engagement rates on digital platforms.
This improved understanding of personalization could lead to new regulatory debates around algorithmic control and user echo chambers.
The ability to finely tune information delivery based on individual history could impact societal consensus and the spread of ideas on a larger scale.
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