论文标题
在线阅读文章
Reading Articles Online
论文作者
论文摘要
我们研究了阅读文章的在线问题，这些问题以动态流的汇总形式列出，例如在新闻源中，作为缩写的社交媒体帖子，或在每日更新有关ARXIV的新文章中。在这种情况下，清单中文章的简要信息仅暗示其内容。我们考虑希望在有限的时间预算中最大化其信息收益的读者，因此要么基于提示立即丢弃文章，要么访问它进行阅读。读者可以在任何时候决定继续当前文章或不可撤销地跳过剩余部分。在这方面，Rao在线阅读文章确实与在线背包问题有很大差异，但也有相似之处。在温和的假设下，我们表明，在随机订单模型中用于在线背包问题的任何$α$ coptitive算法都可以用作黑匣子，以获得$（\ mathrm {e} +α）c $ c $ - 竞争算法的rao，rao，rao，其中$ c $在其中$ c $ c $衡量了对信息的准确性，以尊重该信息的信息。具体来说，使用当前用于在线背包的最佳算法，$ 6.65 <2.45 \ mathrm {e} $ - 竞争性，我们获得了$ 3.45 \ Mathrm {e} c $的上限，rao的竞争比。此外，我们研究了一种自然算法，该算法决定是否基于单个阈值值阅读文章，该算法可以用作人类读者的模型。我们表明，这种算法技术为$ O（c）$ - 竞争性。因此，只要准确性$ c $是常数，我们的算法都是恒定的。
We study the online problem of reading articles that are listed in an aggregated form in a dynamic stream, e.g., in news feeds, as abbreviated social media posts, or in the daily update of new articles on arXiv. In such a context, the brief information on an article in the listing only hints at its content. We consider readers who want to maximize their information gain within a limited time budget, hence either discarding an article right away based on the hint or accessing it for reading. The reader can decide at any point whether to continue with the current article or skip the remaining part irrevocably. In this regard, Reading Articles Online, RAO, does differ substantially from the Online Knapsack Problem, but also has its similarities. Under mild assumptions, we show that any $α$-competitive algorithm for the Online Knapsack Problem in the random order model can be used as a black box to obtain an $(\mathrm{e} + α)C$-competitive algorithm for RAO, where $C$ measures the accuracy of the hints with respect to the information profiles of the articles. Specifically, with the current best algorithm for Online Knapsack, which is $6.65<2.45\mathrm{e}$-competitive, we obtain an upper bound of $3.45\mathrm{e} C$ on the competitive ratio of RAO. Furthermore, we study a natural algorithm that decides whether or not to read an article based on a single threshold value, which can serve as a model of human readers. We show that this algorithmic technique is $O(C)$-competitive. Hence, our algorithms are constant-competitive whenever the accuracy $C$ is a constant.
