Exploiting multidimensional data for web site automation
The continuous growth in size and usage of the World Wide Web poses a number of challenging research problems. This thesis investigates the use of multidimensional data to automate the personalization and management activities of web sites. We pay particular attention to the use of additional dimensions (e.g., contextual or background information) in traditional user-item based top-N recommender systems and propose a multidimensional approach for this purpose. To support our research, we also propose a data warehouse to collect and compile information regarding the activity on a web site in terms of usage, content and structure. We show that, by exploiting multidimensional data, efficient web automation methods can be developed and used to improve the personalization and management of web sites.
multidimensional data web site automation recommender systems data warehouse multidimensional recommender systems