多级反馈的Web链接选择问题：学习与优化
Multi-level Feedback Web Links Selection Problem: Learning and Optimization
摘要：
为网站选择正确的网络链接很重要，因为合适的链接不仅可以提供高吸引力，而且可以增加网站的收入。在这项工作中，我们首先表明网络链接具有内在的\emph{multi-level feedback structure}。例如，考虑$2$级反馈web链接：$1$第一级反馈提供点击率（CTR），而$2$第二级反馈提供潜在收入，它们共同产生复合$2$级收入。我们考虑上下文无关的链接选择问题的主页选择链接，以最大限度地提高总复合$2$级收入，同时保持总$1$第一级反馈高于预设阈值。我们进一步推广的问题与$n~(n\ge2)$级反馈结构的链接。关键的挑战是，链接的多级反馈结构是不可观察的，除非链接是在主页上选择的。据我们所知，我们是第一个模型的链接选择问题作为一个约束多臂强盗问题，并设计了一个有效的链接选择算法，通过学习的链接的多级结构与可证明的\emph{sub-linear}遗憾和违规界。我们发现了两个真实世界的数据集的Web链接的多级反馈结构。我们还进行了大量的实验数据集上比较我们提出的\textbf{LExp}算法与两个国家的最先进的上下文无关的强盗算法，并表明，\textbf{LExp}算法是最有效的链接选择，同时满足约束。
英文摘要：
Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website's revenue. In this work, we first show that web links have an intrinsic \emph{multi-level feedback structure}. For example, consider a $2$-level feedback web link: the $1$st level feedback provides the Click-Through Rate (CTR) and the $2$nd level feedback provides the potential revenue, which collectively produce the compound $2$-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound $2$-level revenue while keeping the total $1$st level feedback above a preset threshold. We further generalize the problem to links with $n~(n\ge2)$-level feedback structure. The key challenge is that the links' multi-level feedback structures are unobservable unless the links are selected on the homepage. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable \emph{sub-linear} regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed \textbf{LExp} algorithm with two state-of-the-art context-free bandit algorithms and show that \textbf{LExp} algorithm is the most effective in links selection while satisfying the constraint.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
