发表在arXiv上的一项新研究探讨了社交网络突出显示的个性化本质，区分了“选择”（用户选择的文档）和“显著性”（文档中被标记的段落）。研究表明，虽然用户历史记录是预测其文档选择的有力指标，但对识别文档内的显著句子影响较小。个性化似乎主要由稳定的主题偏好驱动，而非句子层面的个体细微差别。 AI
影响 表明当前的LLM可能无法有效地实现内容内的细粒度个性化，暗示需要不同的方法。
排序理由 该集群包含多篇讨论研究结果的学术论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →
arXiv:2606.12904v1 Announce Type: cross Abstract: Prior work on a social web highlighter located individuality in selection -- which documents a person chooses to highlight -- but measured it cross-sectionally. We ask the temporal question: is a reader's selection signature a tra…
Prior work on a social web highlighter located individuality in selection -- which documents a person chooses to highlight -- but measured it cross-sectionally. We ask the temporal question: is a reader's selection signature a trait or a state? We freeze each reader's first six m…
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…
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 mar…
Social highlighters let people mark passages that matter to them. We ask how much of an individual is recoverable from these naturalistic traces, using a co-readership identity control (the same document highlighted by many users) that holds document and topic fixed and asks whet…