发表机构
Glasp Inc.（Glasp公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出阅读位置作为个性化高亮预测的基线，通过时间顺序评估证明其优于流行度和相似度方法，并强调评估应遵循时间顺序。
AI 中文摘要
读者在页面上的首次高亮是阅读产品所能获得的最便宜的个人信号。自然的做法是推荐类似早期读者标记的内容，并以流行度作为评判基准。我们认为应被超越的基线是阅读位置。在一个社交高亮平台上的时间顺序评估中（在首次高亮后，涉及1,511个页面上的7,343个读者-页面配对），仅根据读者首次高亮下方的句子排序，而不使用其他读者数据，下一次高亮出现在前五名的概率为47%，而流行度方法为26%，两种相似度方法中较好的一种为29%。该基线取决于目标：在后续所有高亮中，该排序方法逊于流行度；而按与最近高亮的距离进行折扣的流行度方法，在尝试的三种尺度中具有最佳平均精度，在两个目标上均优于流行度和两种相似度方法。在预先指定的比较中，两种相似度方法在后续所有高亮（从一次到五次高亮）的平均精度上均未显示出优于流行度的增益，且排除了+0.01的增益。此外，增益本身并不能表明方法发现了读者的偏好：共享一组偏好的合成读者产生了一个增益，而乱序评估则显示了读者去向的方法。位置结果是探索性的且未经证实。文档内的个性化应以时间顺序评估，并对照阅读位置。
英文摘要
A reader's first highlights on a page are the cheapest personal signal a reading product has. The natural plan is to suggest what similar earlier readers marked, and to judge the result against popularity. We argue that the baseline to beat is reading position. In a time-ordered evaluation on one social highlighting platform (7,343 reader-page pairs on 1,511 pages after one highlight), ranking the sentences just below a reader's first highlight, with no other reader's data, puts the next highlight in the top five 47% of the time, against 26% for popularity and 29% for the better of two similarity methods. The baseline depends on the target: over all later highlights that ranking loses to popularity, while popularity discounted by distance from the latest highlight, at the scale with the best average precision of three tried, beats popularity and both similarity methods on both targets. In a comparison specified in advance, neither similarity method shows a gain over popularity in average precision over all later highlights, from one to five highlights, and a gain of +0.01 is excluded. Nor would a gain by itself show that a method has found a reader's preferences: synthetic readers who share one set of preferences produce one, and an evaluation out of time order shows a method where the reader went. The position results are exploratory and unconfirmed. Personalisation inside a document should be evaluated in time order and against reading position.
Comments13 pages, 1 figure, 5 tables. Ancillary files include the specifications, the results write-ups, the analysis scripts, and the aggregate artifacts every reported number is generated from
