记住我们喜欢什么：基于代理的Web流量模型
Remembering what we like: Toward an agent-based model of Web traffic
摘要：
对总体Web流量的分析表明，PageRank并不能很好地反映人们实际上是如何浏览Web的。使用经验的流量模式产生的1000名用户在两个月的过程中，我们的特点的Web流量，不能再现的马尔可夫模型，其中的目的地是独立于过去的决定的属性。特别是，我们表明，个人用户访问的网站的多样性是更小，更广泛的分布比预测的PageRank模型，链接流量是更广泛的分布比预测，和连续访问同一网站之间的时间是不太广泛的分布比预测。为了解释这些差异，我们引入了一个更现实的导航模型，在该模型中，代理维护个人的书签列表，这些书签被用作隐形传态目标。该模型还可以考虑分支，这是由浏览器功能（如标签和后退按钮）引起的流量属性。该模型再现聚合流量模式，如网站的流行程度，同时也产生更准确的预测的多样性，链接流量和返回时间分布。这个模型第一次让我们能够捕捉到聚合流量测量的极端异质性，同时解释更狭隘的个人用户的浏览模式。
英文摘要：
Analysis of aggregate Web traffic has shown that PageRank is a poor model of how people actually navigate the Web. Using the empirical traffic patterns generated by a thousand users over the course of two months, we characterize the properties of Web traffic that cannot be reproduced by Markovian models, in which destinations are independent of past decisions. In particular, we show that the diversity of sites visited by individual users is smaller and more broadly distributed than predicted by the PageRank model; that link traffic is more broadly distributed than predicted; and that the time between consecutive visits to the same site by a user is less broadly distributed than predicted. To account for these discrepancies, we introduce a more realistic navigation model in which agents maintain individual lists of bookmarks that are used as teleportation targets. The model can also account for branching, a traffic property caused by browser features such as tabs and the back button. The model reproduces aggregate traffic patterns such as site popularity, while also generating more accurate predictions of diversity, link traffic, and return time distributions. This model for the first time allows us to capture the extreme heterogeneity of aggregate traffic measurements while explaining the more narrowly focused browsing patterns of individual users.
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。
