Personalized Human-Computer Interaction

User modeling and adaptive systems deal with creating and maintaining a user model with the aim to adapt interactive systems. User models can be inferred from implicitly observed user behavior or explicitly entered information, such as the user's profile data, the user's current location or items that the user browsed, searched, tagged or bought earlier. Applications of personalization include recommendations of items, location-based services, updates on friend activities, interest-based portal sites, educative games and personalized guidance or help. With the ongoing transition from desktop computers to mobile devices and ubiquitous environments, the need for more and better user modeling and personalization to adapt to changing contexts in various situations is even more important. But this also poses new challenges, including privacy problems and questions of user control. Systems may draw wrong conclusions about a user's search actions, limit functionality due to badly designed personalized menus, or may inadvertently disclose sensitive information to colleagues and friends. In addition, the user experience is becoming more important in a mobile and connected world. It may not be only important to deliver the absolute best recommendations, but have fast and "good enough" recommendations. On the one hand, there is a battle for the attention of users. On the other hand, the cost of wrong adaptation is very high, users may quickly switch to different applications and service, if he or she is getting annoyed. Personalization does not need to be limited to generating lists of recommendations: adaptations such as personalized maps, tailored menus, link annotation and scripting potentially have a greater effect on the user experience. A particular design issue is the explanation of why items are recommended, or which interface elements have been adapted - and how this can be made undone, if needed. And how can one encourage users to inspect and adjust their user profiles, collected information and privacy settings? Topics Topics include but are not limited to: Obtaining user data: logging tools, aggregation of data from social networks and other Web 2.0 services, location tracking, sensor networks Modeling user data: collaborative filtering, cross-application issues, contextualization and disambiguation, use of ontologies and folksonomies Personalization and recommendation: applications in social networks, search, online stores, mobile computing, e-learning, automotive domain, assisting elderly or handicapped persons and other applications areas Privacy issues, transparency, user control and scrutability Adaptive or intelligent user interfaces: adaptive dialogues, menus or other means of interaction, intelligent agents, feedback mechanisms, interaction with ubiquitous environments, new paradigms in human-computer interactions Personalized interaction: approaches to personalize user input or system feedback (involving novel interaction paradigms), related prototypes and studies Adaptive support for learning and teaching: methods and tools for individual support in the knowledge acquisition process, adaptive support for collaborative learning Evaluation and user studies: laboratory studies, empirical studies in the field and analysis of existing corpora of usage data