代理、书签和点击：一个Web流量的主题模型
Agents, Bookmarks and Clicks: A topical model of Web traffic
摘要：
对总体和个人Web流量的分析表明，PageRank是人们如何浏览Web的一个糟糕模型。使用经验的流量模式所产生的一千个用户，我们的特点是不能再现马尔可夫模型的Web流量的几个属性。我们研究了捕获集体行为（如页面和链接流量）的聚合统计数据和个体统计数据（如熵和会话大小）。目前没有一个模型可以同时解释所有这些经验观察。我们表明，所有这些交通模式可以解释一个基于代理的模型，考虑到几个现实的浏览行为。首先，代理维护个人的书签列表（一种非马尔可夫记忆机制），这些书签被用作心灵传输目标。第二，代理可以沿着访问过的链接撤退，这是一种分支机制，也允许我们重现诸如使用后退按钮和重新浏览等行为。最后，代理人通过访问热门的新页面来维持，相邻的页面比遥远的页面更热门。这调节了代理继续浏览或启动新会话的概率，允许我们重新创建不同的会话长度。由此产生的模型是能够再现的集体和个人的行为，我们观察到的经验数据，调和狭隘的集中浏览模式的个人用户与聚合流量测量的极端异质性。这一结果使我们能够确定一些显著的特征，这些特征对于解释我们数据中观察到的浏览模式是必要的和足够的。除了这样一个模型的描述性和解释性的力量，我们的结果可能会导致更复杂的，现实的，有效的排名和爬行算法的方式。
英文摘要：
Analysis of aggregate and individual Web traffic has shown that PageRank is a poor model of how people navigate the Web. Using the empirical traffic patterns generated by a thousand users, we characterize several properties of Web traffic that cannot be reproduced by Markovian models. We examine both aggregate statistics capturing collective behavior, such as page and link traffic, and individual statistics, such as entropy and session size. No model currently explains all of these empirical observations simultaneously. We show that all of these traffic patterns can be explained by an agent-based model that takes into account several realistic browsing behaviors. First, agents maintain individual lists of bookmarks (a non-Markovian memory mechanism) that are used as teleportation targets. Second, agents can retreat along visited links, a branching mechanism that also allows us to reproduce behaviors such as the use of a back button and tabbed browsing. Finally, agents are sustained by visiting novel pages of topical interest, with adjacent pages being more topically related to each other than distant ones. This modulates the probability that an agent continues to browse or starts a new session, allowing us to recreate heterogeneous session lengths. The resulting model is capable of reproducing the collective and individual behaviors we observe in the empirical data, reconciling the narrowly focused browsing patterns of individual users with the extreme heterogeneity of aggregate traffic measurements. This result allows us to identify a few salient features that are necessary and sufficient to interpret the browsing patterns observed in our data. In addition to the descriptive and explanatory power of such a model, our results may lead the way to more sophisticated, realistic, and effective ranking and crawling algorithms.
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。
