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COBRA: A CBR-Based Approach for Predicting Users Actions in a Web Site

机译:COBRA:一种基于CBR的方法来预测网站中的用户操作

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In this paper we describe an original case-based reasoning (CBR) approach, called Cobra, that aims at predicting users requests in a web site. The basic idea underlying the Cobra approach is to model users navigational behavior in a web site by a set of cases. Typically, in a CBR system a case is composed of at least two parts: the situation or the problem part and the solution one. In the Cobra approach the situation part of a case captures a navigation experience within a user navigation session. The solution part is composed of a set of actions that may explain the transition (i.e. the move from one page to another) which follows the navigation experience described in the case situation part. The proposed case structure and the reuse phase enable to predict the access to pages that have never been visited before by any user. This is a very useful feature that matches prediction requirements in real web sites where the structure and the content change frequently over time.
机译:在本文中,我们描述了一种原始的基于案例的推理(CBR)方法,称为Cobra,旨在预测网站中的用户请求。 Cobra方法的基本思想是通过一系列案例来模拟用户在网站中的导航行为。通常,在CBR系统中,案例至少由两部分组成:情境或问题部分以及解决方案部分。在Cobra方法中,案例的情境部分捕获了用户导航会话中的导航体验。解决方案部分由一组动作组成,这些动作可以解释遵循案例情况部分中描述的导航体验的过渡(即,从一页移动到另一页)。提出的案例结构和重用阶段可以预测任何用户从未访问过的页面的访问。这是一个非常有用的功能,可以匹配实际网站中的预测要求,在这些网站中,结构和内容经常随时间变化。

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