Theme issue on adaptation and personalization for ubiquitous computing

Zhiwen Yu, Doreen Cheng, Ismail Khalil, Judy Kay, Dominikus Heckmann
2011 Personal and Ubiquitous Computing  
Ubiquitous computing is a human-centered paradigm that aims to provide users with adaptive and personalized services according to their surrounding context. Adaptation and personalization technologies are thus an important basis of ubiquitous computing. They are also the core for realizing context awareness in pervasive service provisioning. In ubiquitous computing environments, people are surrounded by many networked computers, both fixed (e.g., PCs, TVs) and mobile devices such as PDAs,
more » ... ar phones, etc. People are increasingly able to access their desired content, anytime, anywhere using the available devices. To offer the right information to users at the right time, right place and in the right way is challenging for many reasons, such as varying user interests, heterogeneous environments and devices, dynamic networks, information overload, user privacy, and so on. This theme issue aims to explore adaptation and personalization services and technologies for ubiquitous computing. Submissions to this special issue came from an open call for papers as well as from selected papers presented at the 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010) held at Xi'an, China, October 26-29, 2010. We received a total of 26 submissions of which 8 papers were accepted after three rounds of rigorous reviews. We are grateful to the large number of reviewers who assisted us in the review process; in order to ensure high reviewing standards, three to four reviewers evaluated each paper. The opening paper of this special issue, "Social itinerary recommendation from user-generated digital trails", authored by Hyoseok Yoon, Yu Zheng, Xing Xie, and Woontack Woo received the best paper award of UIC 2010. The paper addresses the problem of planning travel to unfamiliar regions for novice travelers. It proposes recommending a social itinerary by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. It describes an itinerary model in terms of attributes extracted from user-generated GPS trajectories, and a social itinerary recommendation framework that can find and rank itinerary candidates. It also reports the evaluation results using a large set of usergenerated GPS trajectories collected from Beijing, China. The second paper, "TruBeRepec: a trust-behavior-based reputation and recommender system for mobile applications", by Zheng Yan, Peng Zhang, and Robert H. Deng, examines the trustworthiness of mobile applications that are to be recommended to a user. The authors introduce a model of trust behavior for mobile applications based on the results of a large-scale user survey. Several algorithms
doi:10.1007/s00779-011-0441-x fatcat:5c57r5ua6bfx3eayxhh6b2gaue