关联网页以执行信息收集任务
Relating Web pages to enable information-gathering tasks
摘要：
我们认为，网页之间的关系是用户的意图的功能。我们确定了一类Web任务-信息收集-可以通过搜索引擎提供与用户当前正在查看的页面相关的页面链接来促进。我们定义了三种意向关系，分别对应于用户是a）寻找信息源，b）阅读提供信息的页面，还是c）浏览页面作为扩展信息收集过程的一部分。我们表明，这三种关系可以有效地挖掘使用的文本和链接信息的组合，并提供三个评分机制，对应于他们：{ \em SeekRel}，{ \em FactRel}和{ \em SurfRel}。这些评分机制结合了文本和链接信息。我们建立了一组有能力的子网络-每个子网络对应于一个特定的关键字-反映了万维网的互连结构。通过计算这些子网络上的流量来计算分数。根据Kleinberg（1998）关于在超链接环境中为页面分配权限的工作，链接的容量是从它们所连接的节点的{ \em hub}和{ \em authority}值导出的。我们评估了我们的评分机制，通过运行实验从网络上获取的四个数据集。我们呈现了用户对我们的评分机制返回的顶级结果的相关性的评估，并将其与Google的相似页面功能返回的顶级结果以及Dean和Henzinger（1999）提出的{ \em Companion}算法进行比较。
英文摘要：
We argue that relationships between Web pages are functions of the user's intent. We identify a class of Web tasks - information-gathering - that can be facilitated by a search engine that provides links to pages which are related to the page the user is currently viewing. We define three kinds of intentional relationships that correspond to whether the user is a) seeking sources of information, b) reading pages which provide information, or c) surfing through pages as part of an extended information-gathering process. We show that these three relationships can be productively mined using a combination of textual and link information and provide three scoring mechanisms that correspond to them: {\em SeekRel}, {\em FactRel} and {\em SurfRel}. These scoring mechanisms incorporate both textual and link information. We build a set of capacitated subnetworks - each corresponding to a particular keyword - that mirror the interconnection structure of the World Wide Web. The scores are computed by computing flows on these subnetworks. The capacities of the links are derived from the {\em hub} and {\em authority} values of the nodes they connect, following the work of Kleinberg (1998) on assigning authority to pages in hyperlinked environments. We evaluated our scoring mechanism by running experiments on four data sets taken from the Web. We present user evaluations of the relevance of the top results returned by our scoring mechanisms and compare those to the top results returned by Google's Similar Pages feature, and the {\em Companion} algorithm proposed by Dean and Henzinger (1999).
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