Date Added: Sep 2009
The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories.