-------- Forwarded Message --------
*** 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@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 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@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 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@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
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@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
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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