Traffic-based feedback on the web
Traffic-based feedback on the web
复制标题
DOI：
10.1073/pnas.0307539100
复制
发表时间：
2004-04-06
影响因子：
11.1
通讯作者：
Novak, A
中科院分区：
文献类型：
--
作者：
Aizen, J;Huttenlocher, D;Novak, A
高流量网站的使用数据可以揭示有关外部事件和人气激增的信息，这些信息可能无法仅通过分析内容和链接结构来访问。我们认为网站是围绕一组可供购买或下载的项目，考虑，例如，电子商务网站或在线研究论文的集合，我们研究了一个简单的指标，集体用户对一个项目的兴趣，击球率，定义为访问一个项目的描述，导致该项目的收购的分数。我们开发了一个随机模型，用于识别一个项目的击球率经历显着变化的时间点。在对互联网档案馆的使用数据进行的实验中，我们发现这种变化经常以突然的、离散的方式发生，并且这些变化可以与网站上某个项目的突出显示或来自活跃的外部链接器的链接的出现等事件密切相关。通过这种方式，分析活动网站上项目流行度的动态可以帮助描述网站内外发生的一系列事件的影响。
Usage data at a high-traffic web site can expose information about external events and surges in popularity that may not be accessible solely from analyses of content and link structure. We consider sites that are organized around a set of items available for purchase or download, consider, for example, an e-commerce site or collection of online research papers, and we study a simple indicator of collective user interest in an item, the batting average, defined as the fraction of visits to an item's description that result in an acquisition of that item. We develop a stochastic model for identifying points in time at which an item's batting average experiences significant change. In experiments with usage data from the Internet Archive, we find that such changes often occur in an abrupt, discrete fashion, and that these changes can be closely aligned with events such as the highlighting of an item on the site or the appearance of a link from an active external referrer. In this way, analyzing the dynamics of item popularity at an active web site can help characterize the impact of a range of events taking place both on and off the site.