Personalization of E-newsletters Based on Web Log Analysis and Clustering

We present a methodology for the personalization of e-newsletters based on the analysis of user access logs. To approach the problem we have used clustering on the set of users, described by their Web access patterns. Our work is evaluated using a case study with real data from e-newsletters sent by mail to users of a Web portal, and can be adapted to similar situations. Positive results were obtained, indicating that the methodology is able to automatically select contents for a personalized e-newsletter