User Modeling and Adaptivity

ContactTill Plumbaum


The "User Modeling and Adaptivity"-Cluster works on advanced methods and tools that allow collecting, aggregating and understanding user data and user behavior. This user information will then be processed and enriched with semantic information. This enriched information serves as a basis for personalization of adaptive systems and recommender systems as well as for the Smart Layouting of news articles. 

As part of this research, the CC IRML concentrates on the intelligent collection of user behavioral data from web applications. Another core theme is the research on how to aggregate distributed user information. The selected approaches are focused on the application of semantic methods. 

Semantic User Tracking: User behavior is a valuable information source for adaptive systems and to support people using a system. With the advent of the Web 2.0, web applications become more and more dynamic, and the way users can interact with them change. Therefore, the techniques to track the user behavior have to cope with these new challenges and have to be extended with semantic techniques to collect fine-grained data from user interactions to provide better information for adaptive systems.

Aggregation of User Information: Applications typically store their user information in a proprietary format in their own application depended databases. This leads to a distributed web model of a user with several partial UMs in different applications potentially duplicating information. Therefore, the challenge is to solve the heterogeneity of the user models and create one big user model.