Mining Frequent Generalized Patterns for Web Personalization

In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GPClose) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, belong to categories organized in a taxonomy. It takes as input the transaction database and the taxonomy of categories and produces generalized association rules that contain transaction items and/or item categories. This algorithm is particularly useful for personalizing web sites with continuously updated content, such as, blog aggregators, or news portals. In this context, the transaction database contains user click-stream information and the hierarchy of item types is a thematic taxonomy of web pages. The algorithm generates frequent itemsets comprising of both web pages and categories. The results are used to generate association rules and consequently recommendations for the users. We experimentally evaluate the proposed algorithm using web log data collected from a newspaper web site.

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