会议简介

ACM UMAP – User Modelling, Adaptation and Personalization – is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. ACM UMAP is the successor to the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It is sponsored by ACM SIGCHI and SIGWEB, and organized with User Modeling Inc. as the Steering Committee. The proceedings are published by ACM and will be part of the ACM Digital Library.

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征稿信息

重要日期

初稿截稿日期:2019-02-01

摘要截稿日期:2019-01-25

终稿截稿日期:2019-04-03

征稿简介

Conference Tracks

Track 1 - Personalized Recommender Systems

Chairs:

Marko Tkalcic, Free University of Bozen-Bolzano, marko.tkalcic@unibz.it

Alan Said, University of Skövde, alansaid@acm.org

 

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 
- Recommender and personalization system 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
- Novel, Psychology-informed User- and Item-modeling 
 

Track 2 - Adaptive Hypermedia And The Semantic Web

Chairs:

Liliana Ardissono, University of Torino, liliana.ardissono@unito.it

Katrien Verbert, KU Leuven, katrien.verbert@cs.kuleuven.be

 

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, as well as the use of ontologies for information integration. 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 adaptive web
    - Ontology-based user models
    - Semantic social network mining, analysis, representation, and management
    - Crowdsourcing semantics; methods, dynamics, and challenges
    - Semantic Web and Linked Data for adaptation

Track 3 - Intelligent User Interfaces

Chairs:

Li Chen, Hong Kong Baptist University, lichen@comp.hkbu.edu.hk

Jingtao Wang, Google, jingtaow@acm.org  

 

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 characterized 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 visualization, and knowledge representation and reasoning

Topics include (but are not limited to):

 

  • - Adaptive personal virtual assistants (e.g., interaction with robots)
    - Adapting natural interaction (e.g., natural language, speech, gesture)
    - Intelligent user interfaces based on sensor data (UIs for cars, fridges, etc.)
    - 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
    - User-centric studies of interactions with intelligent user interfaces
    - Novel datasets and use cases for intelligent user interfaces
    - Evaluations of intelligent user interfaces

Track 4 - Personalized Social Web

Chairs:

Ilaria Torre, University of Genova, ilaria.torre@unige.it

Osnat Mokryn, omokryn@univ.haifa.ac.il

 

The social web is continuously growing and social platforms are a fundamental part of our life. Mediated communication is becoming the primary form of communication for young people, and adults follow in increasing numbers. Online communication is increasingly enriched by the use of memes, pictures, audio and video, though language (textual and oral) remains a fundamental tool with which people interact, convey their opinions, construct and determine their social identity. Lifelogging data (e.g., health, fitness, food) is growing as well on the social web. This type of personal information source, gathered for private use through personal devices, is now often shared in online communities. These trends open new challenges for research: how to harness the power of collective intelligence and quantified self data in online social platforms to identify social identities, how to exploit continuous feedback threads, and how to improve the individual user experience on the social web. 
We invite original submissions addressing all aspects of personalization, user models building and personal experience in online social systems.

Topics include (but are not limited to):

  • - Personalization of the web experience in social systemsb
    - Adaptations based on personality, society, and culture
    - Personalization algorithms and protocols inspired by human societies
    - Social recommendation 
    - Identifying social identities in social media
    - Social and crowd-generated data for adaptation
    - Personalized information retrieval
    - Exploiting quantified self data on the social web of things
    - Data-driven approaches for personalization
    - Modeling individuals, groups, and communities
    - Collective intelligence and experience mining
    - Pattern and behaviour discovery in social network analysis
    - Opinion mining for user modeling
    - Sentiment analysis
    - Topic modeling for online conversations and short texts
    - Privacy, perceived security, and trust in social systems
    - Ethical issues involved in studying the social web
    - User awareness and control
    - Evaluation methodologies for the social web

Track 5 - Technology-Enhanced Adaptive Learning

Chairs:

Jesús G. Boticario, UNED, jgb@dia.uned.es

Inge Molenaar, Radboud University, i.molenaar@pwo.ru.nl

 

At large there is an on-going “fusion” between humans and technological systems. The ongoing integration of devices into our daily lives furthers the integration of technology in human learning. With technology increasingly gaining more data and intelligence, a new era of technology-enhanced adaptive learning is emerging. Consequently, the interactions between learners, teachers and technology are becoming increasingly complex. Learning is a positioned as a complex human process that involves cognitive, metacognitive, motivational, affective and psychomotor aspects which interact with the learning context. Smart technological solutions are increasingly able to identify and model the learner needs on these five aspects and accordingly provide personalized support that can improve the effectiveness, efficiency and satisfaction of learning experiences.

Current research in artificial intelligence combined with data science and learning analytics bring new opportunities to recognize, and effectively support  individual learners’ needs and orchestrate collaborate and classroom learning with intelligent learning solutions, and augment teachers in blended learning situations. The aim of this track is to foreground the systematic complexity of human learning and use systematic analytic approaches to measure, diagnose and support human learning with technologies. This covers not only formal educational settings, but also lifelong learning requirements (including workplace training) as well as the acquisition of skills informal learning settings (e.g., in daily activities, serious games, sports, healthcare, wellbeing, etc.).

To address the wide spectrum of modeling issues and challenges that can be raised, contributions from various research areas are welcome. Therefore, this track invites researchers, developers, and practitioners from various disciplines to present their innovative adaptive learning solutions, share acquired experience, and discuss the main modeling challenges for technology enhanced adaptive learning.

Topics include (but are not limited to):

 

  • - Domain, learner, teacher and context modeling
    - Modeling cognitive, metacognitive, motivational, affective and psychomotor aspects of learning
    - Diagnosis of learner needs and calibration of support and feedback Adaptive and personalized support for learning
    - Dealing with ethical issues involved in detecting and modeling a wider range of information sources (e.g., information from novel sensing devices, ambient intelligent features) that may affect learning
    - Management of large, open, and public datasets for educational data mining
    - Agent-based learning environments and virtual pedagogical agents
    - Open corpus personalized learning
    - Collaborative and group learning
    - Adaptive technologies to orchestrated classroom Learning
    - Personalized teachers awareness and support tools
    - Multimodal learning analytics to personalize learnin
    - 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-learnin
    - Interoperability, portability, and scalability issues
    - Case studies in real-world educational settings
    - New methodologies to develop user-centered highly personalized learning solutions

Track 6 - Privacy And Fairness

Chairs:

Bart Knijnenburg, Clemson University, bartk@clemson.edu

Esma Aimeur, University of Montreal, aimeur@iro.umontreal.ca

 

Adaptive systems researchers and developers have a social responsibility to care about their users. This involves building, maintaining, evaluating, and studying adaptive systems that are fair, transparent, and protect users' privacy. We invite papers that study, in the context of UMAP, the topics of privacy (as well as innovative means to resolve privacy problems through algorithms, interfaces, or other technical or non-technical means), fairness (covering the spectrum from algorithmic fairness to social implications of adaptive systems), and transparency (as a concept of system usability as well as a means to resolve problems with privacy and fairness). Beyond this we encourage authors to submit to this track any work that ascribes to or advances the general idea of "adaptive systems that care”.

Privacy topics:

  • - Analysis of privacy implications of user modeling
    - Privacy compliance
    - Algorithmic solutions to privacy
    - Architectural solutions to privacy
    - Interactive solutions to privacy
    - Usable privacy for adaptive systems
    - User perceptions of privacy in UMAP applications - Studies of users’ privacy-related behaviors in UMAP applications
    - Descriptions or evaluations of privacy-settings user interfaces
    - Privacy prediction / personalization
    - User-tailored approaches to privacy
    - Privacy education for user modeling
    - Modeling of data protection and privacy requirements
    - Economics of privacy and personal data
    - Measuring privacy

Fairness topics:

  • - Ethical considerations for user modeling
    - UMAP applications for underrepresented groups
    - Cultural differences (e.g. culture-aware user modeling)
    - Bias and discrimination in user modeling
    - Imbalance in meeting the needs of different groups of users
    - Balancing needs of users versus system owners
    - Ethics of explore/exploit strategies or A/B testing
    - ‘Filter bubble’ or ‘balkanization’ effects
    - Enhancing/embracing diversity in user modeling
    - Algorithmic methods for increasing fairness
    - User perceptions of fairness
    - Measuring fairness



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Transparency topics:

  • - User perceptions of transparency
    - Transparent algorithms
    - Interface innovations that increase transparency
    - Explanations for transparency
    - Visualizations for transparency
    - Adaptive systems for self-actualization
    - (User-centric) evaluations of methods that increase transparency
    - Measuring transparency



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Track 7 - Personalized Music Access

Chairs:

Markus Schedl, University of Linz, markus.schedl@jku.at

Nava Tintarev, TU Delft, n.tintarev@tudelft.nl

 

Music access systems (e.g., search, retrieval, and recommendation systems) have experienced a boom during the past decade due to the availability of huge music catalogs to users, anywhere and anytime. These systems record information on user behavior in terms of actions on music items, such as play, skip, or playlist creation and modification. As a result, an abundance of user and usage data has been collected and is available to companies and academics, allowing for user profiling and to create and improve personalized music access. This track addresses unsolved challenges in this area relating to user understanding and modeling, personalization in recommendation and retrieval systems, modeling usage context, and adapting interactive intelligent music interfaces. This track aims to provide a forum for researchers and practitioners for the latest research on​ user modeling and personalization for finding, making, and interacting with music.

Topics include (but are not limited to):

 

  • - Personalized music preference elicitation and preference learning
    - Psychological modeling of music listeners (e.g., personality, emotion,etc.)
    - Subjective perceptions of music (e.g., similarity, mood, tempo) social and cultural aspects of listening behavior (e.g., for group recommenders)
    - Applications for personalized music consumption and creation
    - Personalized playlist generation and continuation (e.g., sequences and transitions)
    - Personalized music interaction and interface paradigms (e.g., visualization, VR)
    - Explainability, transparency, and fairness in personalized music
    - Systems user-centric performance measures (e.g., diversity, novelty, serendipity, etc.)
    - Datasets (including benchmarks) for personalizing music retrieval and recommendation

Track 8 - Personalized Health

Chairs:

Christoph Trattner, University of Bergen, trattner.christoph@gmail.com

David Elsweiler, University of Regensburg, david@elsweiler.co.uk

 

Growing health issues and rising treatment costs mean that technological systems are increasingly important for global health. Personalised systems, tailored to the needs and behaviours of individual patients, are one of the promising approaches to health promotion by encouraging lifestyle change, managing treatment programmes and providing doctors and other healthcare providers with detailed individualized feedback. The challenges to developing such systems, which model user needs and preferences, as well as appropriate medical knowledge to provide assistance and recommendations are plentiful. The diverse technologies which could potentially feature in solutions are equally vast, ranging from AI systems to sensors, from mobile computing, augmented reality and visualization, to mining the web or other data streams to learn about health issues and user behaviour. In this track we invite scholars working in these or related areas to contribute to the discourse on how technology can promote health. This track aims to provide a forum to researchers to discuss open research problems, solid solutions, latest challenges, novel applications and innovative research approaches and in doing so to strengthen the community of researchers working on Personalized Health and attract representatives from from diverse scholarly backgrounds ranging from computer and information science to public health, epidemiology, psychology, medicine, nutrition and fitness.

Topics include (but are not limited to):

  • - Algorithms and Recommendation Strategies to increase health
    - Mobile health
    - Quantified self
    - Applied data analytics and modeling for health
    - Health risk modeling and forecasting
    - Systems for Preventative Measures 
    - Medical Evaluation Techniques
    - Domain Knowledge Representation
    - Behavioral Interventions: Persuasion/Nudging/Behavioral Change
    - HCI, Interfaces and Visualisations for health
    - Regulations and Standards
    - Human/ Expert-in-the-Loop
    - Gamification and Serious Games
    - Privacy, Trust, Ethics
    - Datasets

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