Liliana Ardissono – författare

TV viewers today are exposed to overwhelming amounts of information, and challenged by the plethora of interactive functionality provided by current set-top boxes. To ensure broad adoption of this technology by consumers, future Digital Television will have to take usability issues thoroughly into account. In particular serious attention must be paid to facilitate the selection of content on an individual basis, and to provide easy-to-use interfaces that satisfy viewers' interaction requirements. This volume collects selected research reports on the development of personalized services for Interactive TV. Drawing upon contributions from academia and industry in the US, Europe and Asia, this book represents a comprehensive picture of leading edge research in personalized television.

The 33 revised full papers and 30 poster summaries presented together with papers of 12 selected doctoral consortium articles and the abstracts of 3 invited lectures were carefully reviewed and selected from 160 submissions. The book offers topical sections on adaptive hypermedia, affective computing, data mining for personalization and cross-recommendation, ITS and adaptive advice, modeling and recognizing human activity, multimodality and ubiquitous computing, recommender systems, student modeling, user modeling and interactive systems, and Web site navigation support.

This book constitutes selected papers from the lectures given at the workshops held in conjunction with the User Modeling, Adaptation and Personalization Conference, UMAP 2011, Girona, Spain, in July 2011. The 40 papers presented were carefully reviewed and selected for inclusion in this book. For each workshop there is an overview paper summarizing the workshop themes, the accepted contributions and the future research trends. In addition the volume presents a selection of the best poster papers of UMAP 2011. The workshops included are: AST, adaptive support for team collaboration; AUM, augmenting user models with real worlds experiences to enhance personalization and adaptation; DEMRA, decision making and recommendation acceptance issues in recommender systems; PALE, personalization approaches in learning environments; SASWeb, semantic adaptive social web; TRUM, trust, reputation and user modeling; UMADR, user modeling and adaptation for daily routines: providing assistance to people with special and specific needs; UMMS, user models for motivational systems: the affective and the rational routes to persuasion.