Call for Papers: 2018 26th ACM Conference on User Modeling, Adaptation and Personalization (UMAP’18) ********************** SUBMISSIONS Papers should be submitted through EasyChair (https://easychair.org/conferences/?conf=acmumap2018). Now is three weeks to the abstract submission. The User Modeling, Adaptation, and Personalization (UMAP) 2018 Conference will include high quality peer-reviewed papers related to the above key areas. Maintaining the high quality and impact of the UMAP series, each paper will have three reviews by program committee members and a meta-review presenting the reviewers’ consensual view; the review process will be coordinated by the program chairs in collaboration with the corresponding area chairs. Long (8 pages + references) and Short (4 pages + references) papers in ACM style Peer reviewed, original, and principled research papers addressing both the theory and practice of UMAP and papers showcasing innovative use of UMAP and exploring the benefits and challenges of applying UMAP technology in real-life applications and contexts are welcome. Long papers should present original reports of substantive new research techniques, findings, and applications of UMAP. They should place the work within the field and clearly indicate innovative aspects. Research procedures and technical methods should be presented in sufficient detail to ensure scrutiny and reproducibility. Results should be clearly communicated and implications of the contributions/findings for UMAP and beyond should be explicitly discussed. Short papers should present original and highly promising research or applications. Merit will be assessed in terms of originality and importance rather than maturity, extensive technical validation, and user studies. Separation of long and short papers will be strictly enforced so papers will not compete across categories, but only within each category. Papers that receive high scores and are considered promising by reviewers, but didn’t make the acceptance cut, will be directed to the poster session of the conference and will be invited to be resubmitted as posters. ********************** Keynote Speeches - Interpreting user input intention in natural human computer interaction, by Prof. Yuanchun Shi, Tsinghua University, China - Robots that listen to people's hearts: the role of emotions in the communication between humans and social robots, by Prof. Ana Paiva, University of Lisbon, Portugal ********************** TRACKS - Personalized Recommender Systems Dietmar Jannach, TU Dortmund, Germany Markus Zanker, Free University of Bolzano-Bozen, Italy Personalized, computer-generated recommendations have become a pervasive feature of today’s online world. The underlying recommender systems are designed to help users and providers in a number of ways. From a user’s viewpoint, for example, these systems assist consumers in finding relevant things within large item collections. On the other hand, from a provider’s perspective, recommenders have also shown to be valuable tools to steer consumer behavior. From a technical perspective, the design of such systems requires the careful consideration of various aspects, including the choice of the user modeling approach, the underlying recommendation algorithm, and the user interface. This track aims to provide a forum for researchers and practitioners to discuss open challenges, latest solutions and novel research approaches in the field of recommender systems. Besides the above-mentioned technical aspects, works are also particularly welcome that address questions related to the user perception and the business value of recommender systems. Topics include (but are not limited to): Recommendation algorithms and their evaluation User modeling and preference elicitation Users’ perception of recommender systems Business value of recommendation systems and multi-stakeholder environments Explanations and trust Context-aware recommendation algorithms Recommending to groups of users Case studies of real-world implementations - Adaptive Hypermedia and the Semantic Web Peter Brusilovsky, Univ. of Pittsburgh, USA Geert-Jan Houben, TU Delft, The Netherlands Adaptive hypermedia and adaptive web explore alternatives to the traditional “one-size-fits-all” approach in the development of web and hypermedia systems. Adaptive hypermedia and adaptive web systems build a model of the interests, preferences and knowledge of each individual user, and use this model in order to adapt the behavior of hypermedia and web systems to the needs of that user. Semantic web frequently serves as an infrastructure to enable adaptive and personalized Web systems. Semantic web technology targets the use of explicit semantics and metadata to help web systems perform the desired functionality: this implies the use of linked data from the web, the use of ontologies in models, or the use of metadata in user interfaces. This track aims to provide a forum to researchers to discuss open research problems, solid solutions, latest challenges, novel applications and innovative research approaches in adaptive hypermedia and semantic web. Topics include (but are not limited to): Web user profiles Adaptive navigation support Personalized search Web content adaptation Analytics of web user data Adaptive Web sites and portals Adaptive books and textbooks Social navigation and social search Navigation support in continuous media and virtual environments Usability engineering for adaptive hypermedia and Web systems Novel methodologies for evaluating adaptive hypermedia and Web systems Semantic Web technologies for Web personalization Ontology-based data access and integration/exchange on the adaptive web Ontology engineering and ontology patterns for the adaoptive web Semantic social network mining, analysis, representation, and management Crowdsourcing semantics; methods, dynamics, and challenges Semantic Web and Linked Data for adaptation - Intelligent User Interfaces Shlomo Berkovsky, CSIRO, Australia Markus Schedl, Johannes Kepler University Linz, Austria Intelligent User Interfaces aim to improve the interaction between computer systems and human users by means of Artificial Intelligence. The systems support and complement different types of abilities that are normally unavailable in the context of human-only cognition. Previous work has found that humans do not always make the best possible decisions when working together with computer systems. By designing and deploying improved forms of support for interactive collaboration between human decision makers and systems, we can enable decision making processes that better leverage the strengths of both collaborators. More generally this research track can be characterised by exploring how to make the interaction between computers and people smarter and more productive, which may leverage solutions from human-computer interaction, data mining, natural language processing, information visualisation, and knowledge representation and reasoning. Topics include (but are not limited to): Adaptive personal virtual assistants (e.g., Siri, Cortana, Alexa) Adapting natural interaction (e.g., natural language, speech, gesture) Multi-modal interfaces (speech, gestures, eye gaze, face, physiological info, etc.) Intelligent wearable and mobile interfaces Smart environments and tangible computing Transparency and control of decision support systems (e.g., semi-autonomous systems) Explainable intelligent user interfaces Affective and aesthetic interfaces Tailored persuasion and argumentation interfaces Tailored decision support (e.g., over- and under-reliance in uncertain domains) Adaptive information visualization Scalability of intelligent user interfaces to access huge datasets Novel methodologies and real-world implementations of IUI User-centric studies of interactions with intelligent user interfaces Novel datasets and use cases for intelligent user interfaces - Technology-Enhanced Adaptive Learning Olga Santos, UNED, Spain Carla Limongelli, Università Degli Studi Roma Tre, Italy Learning is a very complex human process that involves cognitive, affective and psychomotor aspects. Smart technological solutions are expected to identify and model the learning needs in these three aspects and provide personalized adapted support that can improve the effectiveness and efficiency of learning experiences. Technological innovations bring new opportunities to recognize learner’s needs and how to orchestrate suitable learning solutions, with and without the involvement of the teacher. This covers not only formal educational settings, but also lifelong learning requirements (including workplace training) as well as the acquisition of skills (e.g., in daily activities, serious games, etc.). To address the wide spectrum of modeling issues and challenges that can be raised, contributions from various research areas are necessary. Therefore, this track invites researchers, developers, and practitioners from various disciplines to present their innovative learning solutions, share acquired experience, and discuss the main modeling challenges for personalized adaptive learning. Topics include (but are not limited to): Domain, learner, teacher and context modeling Modeling cognitive, affective and psychomotor aspects of learning Adaptive and personalized support for learning, diagnosis and feedback Agent-based learning environments and virtual pedagogical agents Open corpus personalized learning Collaborative and group learning UMAP aspects in specific learning solutions: educational recommender systems, intelligent tutoring systems, serious games, personal learning environments, MOOCs,... Wearable technologies and augmented reality in adaptive personalized learning Processing collected data for UMAP: educational data mining, learning analytics, big data, deep learning… Semantic web and ontologies for e-learning Interoperability, portability, and scalability issues Case studies in real-world educational settings - Personalized Social Web Cecile Paris, CSIRO, Australia Julita Vassileva, University of Saskatchewan, Canada The massive uptake of numerous social media platforms on the web gave rise the Social Web, where people can obtain, disseminate and share information, interact, collaborate and form communities (social networks), through a variety of means. A number of research questions arise, for example how to model the flow of information through the social web, understand people’s experiences and the effectiveness of online social networks, identify users or communities with behaviour potentially harmful to themselves or others, gain insights into society, or design, develop and evaluate automated personalization tools to improve the individual user experience. We invite original submission addressing all aspects of personalization and personal experience in online social systems. Topics include (but are not limited to): Personalization of the web experience in social systems Social and crowd-generated data for personalization Wisdom of the crowd, human computation and collective intelligence for personalization Incentives for participation and persuasion in online communities Use of online social data for personalized offline experiences Behavior modeling of individuals, groups, and communities Dynamics of social collaborative systems Analysis of information flows in social networks Opinion mining and social media analytics Biases and individual/local perception in social systems Adaptations based on personality, society, and culture Privacy, perceived security, and trust in social systems Ethical issues involved in studying the social web Personalization algorithms and protocols inspired by human societies Machine learning for personalization Evaluation methodologies for the social web ********************** IMPORTANT DATES Submission due Abstract: February 25, 2018 Full paper: March 4, 2018 Notification: Apri 8, 2018 Camera-ready: May 6, 2018 Note: The submissions times are 11:59pm Hawaii time. ********************** FORMAT DETAILS AND PUBLICATION Page limits: Long papers - 8 pages + references; Short pages: 4 pages + references. Note that the references do not count towards page limits. Papers that exceed the page limits or formatting guidelines will be returned without review. Submissions should be single blinded, i.e. authors names should be included in the submissions. Papers must be formatted using the ACM SIG Standard (SIGCONF) proceedings template: https://www.acm.org/publications/proceedings-template. Please note that ACM changed its templates at the start of 2017, so please ensure that you use the new template and do not reuse an old template. All accepted papers will be published by ACM and will be available via the ACM Digital Library. At least one author of each accepted paper must register for the conference and present the paper there. AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)