Contextualization, user modeling and personalization in the social web: from social tagging via context to cross-system user modeling and personalization

Social Web stands for the culture of participation and collaboration on the Web. Structures emerge from social interactions: social tagging enables a community of users to assign freely chosen keywords to Web resources. The structure that evolves from social tagging is called folksonomy and recent research has shown that the exploitation of folksonomy structures is beneficial to information systems. In this thesis we propose models that better capture usage context of social tagging and develop two folksonomy systems that allow for the deduction of contextual information from tagging activities. We introduce a suite of ranking algorithms that exploit contextual information embedded in folksonomy structures and prove that these contextsensitive ranking algorithms significantly improve search in Social Web systems. We setup a framework of user modeling and personalization methods for the Social Web and evaluate this framework in the scope of personalized search and social recommender systems. Extensive evaluation reveals that our context-based user modeling techniques have significant impact on the personalization quality and clearly improve regular user modeling approaches. Finally, we analyze the nature of user profiles distributed on the Social Web, implement a service that supports cross-system user modeling and investigate the impact of cross-system user modeling methods on personalization. In different experiments we prove that our cross-system user modeling strategies solve cold-start problems in social recommender systems and that intelligent re-use of external profile information improves the recommendation quality also beyond the cold-start.