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

Website https://www.um.org/umap2019/ | Edit Freely

Category Personalized Recommender Systems; Adaptive Hypermedia And The Semantic Web; Intelligent User Interfaces; Personalized Social Web; Technology-Enhanced Adaptive Learning; Privacy And Fairness; Personalized Music Access; Personalized Health

Deadline: January 25, 2019 | Date: June 09, 2019-June 12, 2019

Venue/Country: Golden Bay Beach Hotel 5*, Larnaca, Cyprus

Updated: 2018-09-20 22:10:03 (GMT+9)

Call For Papers - CFP

27th ACM International Conference on User Modeling, Adaptation

and Personalization (ACM UMAP 2019)

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.

CONFERENCE TRACKS

Track 1 - Personalized Recommender Systems

Chairs:

Marko Tkalcic, Free University of Bozen-Bolzano, marko.tkalcicunibz.it

Alan Said, University of Skövde, alansaidacm.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.ardissonounito.it

Katrien Verbert, KU Leuven, katrien.verbertcs.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, lichencomp.hkbu.edu.hk

Jingtao Wang, Google, jingtaowacm.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.torreunige.it

Osnat Mokryn, omokrynuniv.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, jgbdia.uned.es

Inge Molenaar, Radboud University, i.molenaarpwo.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, bartkclemson.edu

Esma Aimeur, University of Montreal, aimeuriro.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.schedljku.at

Nava Tintarev, TU Delft, n.tintarevtudelft.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.christophgmail.com

David Elsweiler, University of Regensburg, davidelsweiler.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.

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, Posters, Late Breaking Results and Theory, Opinion and

Reflection works, as they have different deadlines and submission

requirements.