Connectionists: The 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2019): Last Call for Papers

[George Angelos Papadopoulos]

*** LAST CALL FOR PAPERS *** 

27th ACM International Conference on User Modeling, Adaptation
and Personalization (ACM UMAP 2019)

“Making Personalization Transparent: Giving control back to the User"

Golden Bay Beach Hotel 5*, Larnaca, Cyprus, June 9-12, 2019

https://www.um.org/umap2019/


Abstracts due:  January 25, 2019 (mandatory)
Papers due:     February 1, 2019



BACKGROUND AND SCOPE

ACM UMAP, "User Modeling, 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 sponsored by ACM
SIGCHI and SIGWEB. The proceedings are published by ACM and will be part
of the ACM Digital Library.

ACM UMAP covers a wide variety of research areas where personalization
and adaptation may be applied. This include (but is in no way limited to) a
number of domains in which researchers are engendering significant
innovations based on advances in user modeling and adaptation,
recommender systems, adaptive educational systems, intelligent user
interfaces, e-commerce, advertising, digital humanities, social networks,
personalized health, entertainment, and many more. 

This year the conference hosts three new tracks, one on privacy and
fairness, one on personalized music access, and one on personalized health.


CONFIRMED INVITED SPEAKERS

• Marios Avraamides, University of Cyprus
• Judith Masthoff, Utrecht University
• Mounia Lalmas-Roelleke, Spotify London


CONFERENCE TRACKS

Track 1 - Personalized Recommender Systems

Chairs:
Marko Tkalcic, Free University of Bozen-Bolzano, marko.tkalcic at unibz.it
Alan Said, University of Skövde, alansaid at 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 at unito.it
Katrien Verbert, KU Leuven, katrien.verbert at 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 at comp.hkbu.edu.hk
Jingtao Wang, Google, jingtaow at 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 at unige.it
Osnat Mokryn, omokryn at 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 systems
• 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 at dia.uned.es
Inge Molenaar, Radboud University, i.molenaar at 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 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
• New methodologies to develop user-centered highly personalized learning
      solutions

Track 6 - Privacy And Fairness

Chairs:
Bart Knijnenburg, Clemson University, bartk at clemson.edu
Esma Aimeur, University of Montreal, aimeur at 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

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

Track 7 - Personalized Music Access

Chairs:
Markus Schedl, University of Linz, markus.schedl at jku.at
Nava Tintarev, TU Delft, n.tintarev at 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 at gmail.com
David Elsweiler, University of Regensburg, david at 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


SUBMISSION AND REVIEW PROCESS

Papers should be submitted through EasyChair:
https://easychair.org/conferences/?conf=acmumap2019

The ACM User Modeling, Adaptation, and Personalization (ACM UMAP) 2019
Conference will include high quality peer-reviewed papers related to the
above key areas. Maintaining the high quality and impact of the ACM 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.

For both long papers and short paper submissions, it is not required to
anonymize the manuscripts, i.e., ACM UMAP will apply a single-blind
reviewing process.

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.

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.)


IMPORTANT DATES

• Abstract: January 25, 2019 (mandatory)

• Full paper: February 1, 2019

• Notification: March 11, 2019

• Camera-ready: April 3, 2019

• Adjunct proceedings, camera ready: April 15, 2018

Note: The submissions times are 11:59pm AoE time (Anywhere on Earth).


GENERAL CHAIRS

• George A. Papadopoulos, University of Cyprus, Cyprus

• George Samaras, University of Cyprus, Cyprus

• Stephan Weibelzahl, PFH Private University of Applied Sciences,
Göttingen, Germany 


RELATED EVENTS

Separate calls will be later sent for Workshops and Tutorials, Doctoral
Consortium, Demos, Late Breaking Results and Theory, Opinion and
Reflection works, as they have different deadlines and submission
requirements.

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