Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting
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Computer Science > Information Retrieval
arXiv:2606.10398 (cs)
[Submitted on 9 Jun 2026]
Title:Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting
View a PDF of the paper titled Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting, by Kazuki Nakayashiki and 1 other authors
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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 person's history identifies which documents in a co-reading neighborhood are theirs, with an own-versus-other gap of +0.169 against community negatives and +0.119 against topic-matched hard negatives (both highly significant); a content-based arm suggests the signal is not purely title-driven but is largely thematic. This is comparable to the span-level selection signal (+0.14) from our prior work: the selection signal is of comparable magnitude across altitudes (+0.12 to +0.17), most of it stable topic preference. At the sentence altitude, a two-stage personalized auto-highlight (an impersonal model proposes candidates, a personal model re-ranks them) does not improve on its impersonal baseline: two off-the-shelf zero-shot LLMs, including a frontier model, predict highlight locations worse than a lead baseline, and personal re-ranking is beaten by the salience order even on the highest-recall candidate pool, so the null is not merely a Stage-1 ceiling artifact. Measurable personalization appears primarily at the selection layer: modest (~+0.13), topic-dominated, with no reliable gain at the salience layer. We also surface a control-in-negatives bias that inflated our document gap to a spurious +0.227 until audited. Going beyond the shared salience layer may be better approached by aggregating individuals than by personalizing them harder.
| Comments: | 9 pages, 1 figure, 3 tables |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2606.10398 [cs.IR] |
| (or arXiv:2606.10398v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10398
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting, by Kazuki Nakayashiki and 1 other authors
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