Call for Papers

Due to the ongoing COVID-19 pandemic, we are planning for a hybrid conference and will accommodate online presentations as needed.

ACM UMAP – User Modeling, Adaptation and Personalization – is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users, and that collect, represent, and model user information. ACM UMAP is sponsored by ACM SIGCHI and SIGWEB, and organized with User Modeling Inc. as the core Steering Committee, extended with past years’ chairs. The proceedings are published by the 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. The main theme of UMAP 2022 is “User control in personalized systems”. Specifically, we welcome submissions related to user modeling, personalization, and adaptation in all areas of personalized systems, with an emphasis on how to balance adaptivity and user control. Below we present a short (but not prescriptive) list of topics of importance to the conference. 

We acknowledge that some research might be influenced by constraints imposed by Covid-19 (e.g., difficulty of running lab studies). Thus, we welcome submissions introducing novel methodologies arising from a need to conduct research in new ways.

All accepted papers will be published by ACM and will be available via the ACM Digital Library. Papers will be accessible from the UMAP ’22 web site through ACM OpenToc Service for one year after publication in the ACM Digital Library. To be included in the Proceedings, at least one author of each accepted paper must register for the conference and present the paper there.

ACM UMAP is co-located and collaborates with the ACM Hypertext conference. UMAP takes place one week after Hypertext, and uses the same submission dates and formats. We expect authors to submit research on personalized systems to UMAP and invite authors to submit their Web-related work without a focus on personalization to the Hypertext conference. The two conferences will organize one shared track on personalized recommender systems (same track chairs and PC, see the track description).

Important Dates

Conference Topics

Submission Instructions

Important Dates

  • Paper Abstracts:   February 10, 2022 (mandatory)
  • Full paper:             February 17, 2022
  • Notification:          April 11, 2022

Note: The submissions deadlines are at 11:59pm AoE time (Anywhere on Earth)

Conference Topics

We welcome submissions related to user modeling, personalization, and adaptation in any area. The topics listed below are not intended to limit possible contributions. Final decisions will be made on the basis of suitability for, and fit to, the overall conference, rather than for specific tracks. Additionally, there is no quota for the maximal number of accepted papers per track. 

Personalized Recommender Systems*

Track Chairs:

Osnat ‘Ossi’ Mokryn (University of Haifa, Israel), Eva Zangerle (University of Innsbruck, Austria) and Markus Zanker (University of Bolzano, Italy)

(*) This is a joint track between ACM UMAP and ACM Hypertext (same track chairs, overlapping PC). Authors planning to contribute to this track can submit to either conference, depending on their broader interest in either Hypertext or UMAP. Papers will be presented at the conference the paper was submitted to, but attendees will be able to join the sessions of both the UMAP conference and the Hypertext conference. Track chairs will invite selected submissions to be extended for a special issue in New Review of Hypermedia and Multimedia (Taylor & Francis). 

Personalized, computer-generated recommendations have become a pervasive feature of today’s online world. From the traditional book and movie recommendations, to suggestions of what we should eat and wear or where we should travel, recommender systems are seamlessly embedded in our daily lives. The underlying recommender systems are designed to help users and providers in a number of ways. From a user’s viewpoint, these systems assist consumers by identifying relevant and appealing items (e.g., products and services) within large collections. From a provider’s perspective, recommender systems have shown to be valuable tools to steer consumer behavior. Regardless of who are the main stakeholders, the design of recommender systems requires the careful consideration of various aspects, including the choice of the user modeling approach, the recommendation algorithm itself, 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. In addition to mature research works addressing any of the aforementioned technical aspects pertaining to recommendations, we also particularly welcome research contributions that address questions related to the user perception and the business value of recommender systems.

Topics include (but are not limited to):

  • User modeling and preference elicitation
  • Recommendation algorithms including aspects of scalability and performance
  • Context-aware recommender systems (including temporal, social, and geographical)
  • Psychological aspects of recommendation (e.g., psychologically-informed user- and item-modeling and recommendation perception)
  • Business value of recommendation systems and multi-stakeholder environments
  • Responsible recommendation, including algorithmic bias and fairness, filter bubbles, ethics, and privacy
  • Evaluation of personalized recommender systems
  • Explanations, transparency, and trust for recommender systems
  • Conversational and sequence-aware Recommender Systems
  • Case studies of real-world implementations
  • Recommendation in non-traditional domains (e.g., education, health, e-governance, tourism, fashion, energy)

Adaptive Hypermedia, Semantic, and Social Web

Track Chairs:

Alexandra I. Cristea (Durham University, UK) and Peter Brusilovsky (University of Pittsburgh, US)

Adaptive hypermedia and adaptive web systems adapt the appearance, content or functionality provided by the system to the interests, preferences, knowledge and other characteristics of each individual user. This adaptation is frequently based on Semantic and Social Web technologies.

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. 

The social web builds on social platforms to mediate communication among people. 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. Social platforms frequently offer their users personalized experience by leveraging large volumes of data collected from past users.

While these three areas support a partial perspective on user communication and exploration, their convergence is bringing new research opportunities to connect users and information items, and by exploiting social media for a richer communication among people. However, this convergence also raises new issues, such as the sharing of personal information, possibly gathered for private use through personal devices. 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. Privacy and trust are important issues that arise, since the social web also helps spread misinformation that has harmful effects on individuals and the community. 

This track aims to provide a forum to researchers to discuss open research problems, solid solutions, the latest challenges, novel applications, and innovative research approaches in adaptive hypermedia, semantic and 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):

  • Adaptive navigation support
  • Personalized web search
  • Data-driven user-modeling approaches for web content personalization
  • Usability engineering for adaptive hypermedia and (social) web systems
  • Semantic Web and linked data technologies for web adaptation and personalization
  • Personalization and explanations based on knowledge graphs
  • Social network mining: pattern discovery, analysis, representation, and management
  • Social navigation and social search
  • Advanced user interfaces, such as VR and AR, for Adaptive Hypermedia and the social web
  • Identifying social identities in social media
  • Exploiting quantified self data on the (social) web of things
  • Modeling individuals, groups, and communities
  • Crowdsourcing and collective intelligence
  • Applications of adaptive hypermedia, semantic and social web
  • Privacy, perceived security, and trust and distrust in (social) web systems
  • Ethical issues involved in studying hypermedia and the social web
  • User awareness and control, (social) visualization
  • Evaluation methodologies for adaptive hypermedia and semantic, social web

Intelligent User Interfaces

Track chairs:

Elisabeth Lex (Graz University of Technology, Austria) and Marko Tkalcic (University of Primorska, Slovenia)

Intelligent user interfaces aim to improve the interaction between computer systems and human users by means of artificial intelligence. These 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 topic 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):

  • User models for adapting user interfaces
  • Adapting natural interaction (e.g., natural language, speech, gesture)
  • Intelligent user interfaces based on sensor data (e.g., UIs for cars, fridges, and IoT)
  • Multimodal interfaces (e.g., combining multiple modalities, such as speech, gestures, eye gaze, face, physiological info)
  • Intelligent wearable and mobile interfaces
  • Affective and aesthetic interfaces
  • User interfaces for supporting human decision-making (e.g., decision strategies)
  • Adaptive / intelligent information visualization
  • Scalability of intelligent user interfaces to access huge datasets
  • Reproducibility (including benchmarks, datasets, and challenges)
  • Interfaces for personalized systems (e.g. recommendation systems, human-centered AI systems)
  • Explanations (e.g. explainable interfaces, explanations for personalized systems)
  • AI-driven interfaces (e.g., conversational, games)
  • Collaborative multi-user interfaces
  • Biases in user interfaces (e.g., cognitive biases, biases due to the interface design)
  • Evaluation of user interfaces (e.g., user-centric studies of interactions with intelligent user interfaces)
  • Application-specific intelligent user interfaces (e.g., news, music)

Technology-Enhanced Adaptive Learning

Track chairs:

Judy Kay (University of Sydney, Australia) and Sharon Hsiao (Santa Clara University, US)

Learning is a complex human process that involves cognitive, metacognitive, motivational, affective, and psychomotor aspects that interact with the learning context. Technology is playing an increasing role in the learning process, and technology-enhanced adaptive learning has the potential to support this process in a variety of ways.

By modelling the cognitive, metacognitive, motivational, affective and psychomotor aspects of learning and providing personalized support, technological solutions can improve the effectiveness and efficiency of learning experiences, as well as learner/teacher satisfaction.

Technological innovations bring new opportunities to recognize learners’ needs and to orchestrate suitable learning solutions, with and without the involvement of the teacher. This covers not only formal educational settings, but also lifelong learning requirements (including workplace training) as well as the informal acquisition of skills (e.g., in daily activities and serious games).

Technology-enhanced adaptive learning is inherently interdisciplinary, covering a wide spectrum of modeling issues and challenges from different perspectives. Therefore, this track invites researchers, developers, and practitioners from various disciplines to present their innovative learning solutions, share acquired experience, and discuss their modeling challenges for personalized 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 with intelligent tutoring systems and personal learning environments
  • 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
  • Educational data mining and learning analytics (including deep learning and the management of large, open, and public datasets)
  • Agent-based learning environments and virtual pedagogical agents
  • Collaborative and group learning (including adaptive technologies to support orchestrated classroom learning)
  • Personalized teacher awareness and support tools
  • Multimodal learning analytics
  • User modeling and adaptation in serious games
  • User modeling and adaptation in MOOCs
  • User control for personalized learning
  • Wearable technologies and augmented reality in personalized immersive 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 personalized learning solutions
  • Open Learner Models (OLMs), interfaces that enable learners to scrutinise and control their learner model and its use

Fairness, Transparency, Accountability, and Privacy

Track chairs:

Bamshad Mobasher (DePaul University College of Computing and Digital Media, US) and Munindar P. Singh (NC State University, US)

Adaptive systems researchers and developers have a social responsibility to care about the impact of their technologies on individual people (users, providers, and other stakeholders) and on society. This involves building, maintaining, evaluating, and studying adaptive systems that are fair, transparent, respectful of users’ privacy, and beneficial to society. The importance of these topics is highlighted by their presence in ethical guidelines such as the ACM Code of Ethics (particularly Sections 1.2 (Avoid Harm), 1.4 (Be fair), and 1.6 (Respect privacy)), as well as their influence on law and regulation such as the GDPR.

Fairness in modeling and responding to user needs includes a spectrum from algorithmic fairness to social implications of adaptive systems. For this CFP, fairness includes diversity, equity, and inclusion. Fairness also includes the ability to mitigate different forms of bias inherent in learning algorithms or in the data from which they learn. Privacy includes giving users control over how their information is captured, retained, used, and shared as a result of their interactions with technical artifacts and through those artifacts with service providers, other users, government agencies, and other entities. Transparency includes exposing key aspects of decision making and information storage by a technical artifact and associated social entities, such as service providers. Accountability concerns holding the relevant stakeholders to account for deviations from, including violations of, any applicable social norms and ethical guidelines. Accountability thus includes mechanisms by which users and others may identify such deviations, demand and produce explanations, and contest such explanations. Together, these properties are sustained through models and methods realized through combinations of algorithms and nontechnical approaches that tackle tradeoffs between the above properties and with other elements of user experience (such as ease of use, efficiency and confidence).

Topics of interest include (but are not limited to):

  • Perceptions and expectations of users, providers, and other stakeholders regarding fairness, privacy, transparency, and accountability
  • Bias and discrimination in user modeling
  • Explainable and scrutable user modeling
  • Models and methods for mitigating bias and for enhancing fairness, accountability, transparency, and privacy
  • Methods and metrics for measuring fairness, accountability, transparency, and privacy
  • Diversity and cultural differences in user modeling
  • Experiences with applications targeted at under-served groups
  • Risks in user modeling including filter bubbles and balkanization of the Internet
  • User-centric evaluation of explanations, visualizations, and other interface innovations for transparency
  • Models and methods for legal compliance of user modeling and personalization systems
  • Models and methods  for users to understand and correct system errors
  • Auditing fairness, safety, privacy, public benefit, or other concerns
  • Analysis of implications of  development methodologies on fairness, privacy, transparency, and accountability
  • Modeling of requirements for fairness, privacy, transparency, and accountability, including the economics of privacy and personal data

Personalization for Persuasive and Behavior Change Systems

Track chairs:

Julita Vassileva (University of Saskatchewan, Canada) and Panagiotis Germanakos (SAP SE, Germany)

Changing human behavior is a crucial step towards the solution of many societal problems. Persuasive and behavior change systems are intentionally designed to support people to achieve behavior change objectives. Research in the area of persuasive and behavior change systems has attracted increasing interest from both practitioners and researchers due to the increasing realization of the important role interactive technologies can play in assisting and motivating people to achieve their various goals and objectives. Persuasive and behavior change systems have found applications in various domains including health and wellness, safety and security, environmental sustainability, education, commerce and politics. Research has continuously highlighted the importance of personalizing these systems. Personalization of persuasive and behavior change systems adapts and tailors these systems to increase their relevance, motivational appeal, user experience, and hence their overall effectiveness at empowering and assisting people to achieve their goals, which are core final aims of user modeling, and adaptation and personalization of systems.

We invite original submissions addressing the areas of personalization and tailoring for persuasive technologies, including but not limited to personalization models, user models, computational personalization, design and evaluation methods, and personal experience designing personalized and adaptive behaviour change technologies.

Topics include (but are not limited to):

  • Frameworks and models for developing personalized persuasive technology
  • Methods and metrics for evaluating the effectiveness of personalized persuasive technology
  • Long-term evaluation and evidence of long-term effects of personalized persuasive technology
  • Methods for large-scale computational personalization of persuasive systems
  • Systematically investigating and highlighting the difference between system-controlled personalization and user-controlled personalization
  • The relationships between individual characteristics (user model types) and effectiveness of various persuasive technology features
  • Personalization of persuasive approaches considering individual differences and contextual/situational variables (and comparison of their effectiveness)
  • Personalization to personality, gender, age,culture, persuadability, player types, emotional states, contextual/situational variables (and comparisons of their effectiveness)
  • How to balance the cost and benefit of personalizing persuasive technology
  • How to develop ethical and privacy-sensitive personalized persuasive technology
  • Transparency and fairness of persuasive technology
  • Challenges and limitations of implementing personalized persuasive technology and possible solutions
  • Case studies and examples of personalized persuasive technologies, describing both successes and failures

Virtual Assistants and Personalized Human-robot Interaction

Track chairs:

Radhika Garg (Syracuse University, US) and Cristina Gena (University of Torino, Italy)

Virtual assistants, such as chatbots, are being employed to support information search and exploration, as well as to mediate interaction with users. Human-robot interaction is also increasingly attracting attention as a new model to support user activity and empowerment, with particular attention to people with physical impairments who can benefit from assistance during the execution of tasks. In regards to both virtual assistants and robots, people can also benefit from their companionship. In both contexts, personalization is a key element to support the adaptation of the system to personal interests, idiosyncrasies, and needs.

A personalized and adaptive interaction strongly relies on the learning a computational model of human behavior and on its integration into the decision-making algorithms of the robot. This includes also the possibility of endowing the robot with meta-cognition capabilities such as the capability of reasoning on the other individuals’ intentions, desires, and beliefs, as well as their internal states, personality, and emotions (often referred to as Theory of Mind – ToM). The ability of a robot to adapt its behavior according to social expectations, specific cultural norms, and possible individual preferences, will determine the success and large-scale use of such robotics application.

This track aims at investigating new models and techniques for the adaptation of these synthetic companions to the individual user.

Topics include (but are not limited to):

  • User-tailored virtual assistant-based interaction paradigms
  • Opportunities for robot/assistant personalization (e.g. personality, assertiveness, appearance, level of support)
  • Personalized robots or virtual assistants to support individual or collaborative work (e.g. in an employment or domestic context)
  • Personalized robots or virtual assistants to in a multi-user context (e.g., domestic context, schools, health centers)
  • Personalized advice-giving robots or virtual assistants
  • Personalized dialogue with robots
  • Agent-based interfaces for “smart” environments (e.g. smart speakers, smart home assistants)
  • Virtual assistants for mobile interactions (e.g. in smartphones or cars)
  • User modeling in human-robot / human-assistant interaction
  • Personalized robots / virtual assistants for learning or play
  • The use of personalized robots or virtual assistants for companionship
  • Culture-sensitive virtual assistants and robots
  • User-adaptive  assistive social  robots to support people with disabilities
  • User-centric evaluation methods for personalized human-robot or human-assistant interactions
  • User cognitive state assessment and monitoring
  • Activity, intention, and emotion recognition
  • Adaptation in physical/virtual interaction
  • Cognitive Architectures and Theory of Mind for adaptive interaction
  • Reinforcement learning for robotic adaptation
  • Adaptation in multimodal interaction
  • Non-verbal social signals in adaptation
  • Affective and emotion-adapted HRI

Research Methods and Reproducibility

Track chairs:

Odd Erik Gundersen (Norwegian University of Science and Technology, Norway) and Dietmar Jannach (University of Klagenfurt, Austria)

Research on user modeling and adaptive systems has greatly evolved during the last decades, but important challenges remain regarding the systematic comparison of different systems, the development of universally accepted and applied methodologies, and the use of standardized metrics to evaluate the acceptability and usefulness of systems to the user.

This track accepts works on methodologies for the evaluation of personalized systems, benchmarks, measurement scales, with particular attention to reproducibility of results and of techniques.

Topics include (but are not limited to):

  • “Systemization of knowledge” papers, including systematic literature reviews and meta-analyses of research methods, practices, and outcomes
  • Standardization of evaluation methodologies for personalized systems
  • User modeling benchmarks
  • Measurement scales for user-centric evaluation
  • Offline-online performance studies
  • Evaluation approaches (e.g., methodologies, metrics, protocols, study designs) for “difficult” scenarios, such as
    • conversational systems
    • multi-stakeholder recommendation
    • longitudinal effects of personalized systems
    • application-specific challenges
    • fairness and responsible personalization and adaptation
    • explainable AI
  • Replications of past UMAP work and reproducibility studies
  • Resource papers, e.g., new datasets, new software frameworks
  • Data-centered questions, e.g., quality, biases, noise, representativeness
  • Theory and practice papers, industry challenges in terms of evaluation
  • Reflective works on and guidelines for the reproducibility of UMAP research
  • Reflections on evaluation methods, e.g., 
    • which methods do we have, 
    • when to use what, 
    • limitations of simulations and user studies,
    • pros and cons,
    • gaps, little explored techniques

Submission and Review Process

Papers will be submitted through EasyChair.

All submissions and reviews will be handled electronically.

Content expectations

Reviewers will evaluate papers based on their significance, originality, rigor, and contribution to the field. Papers that are out of scope, incomplete, or lack sufficient evidence to support the basic claims, may be rejected without full review.

Papers should report on original and substantial contributions of lasting value. Described work should concern theory and/or practice of UMAP. Moreover, papers showcasing innovative use of UMAP and exploring the benefits and challenges of applying UMAP technology in real-life applications and contexts are welcome. 

Evaluations of proposed solutions/applications must be commensurate with the claims made in the paper. Depending on the intended contribution, this may include simulation studies, offline evaluations, A/B tests, or controlled user experiments. 

Research procedures and technical methods should be presented in sufficient detail to ensure scrutiny and reproducibility. We recognize that user data may be proprietary or confidential, but we encourage the sharing of (anonymized, cleaned) data sets, data collection procedures, and code. 

Results should be clearly communicated and implications of the contributions/findings for UMAP and beyond should be explicitly discussed. A discussion of the ethical considerations behind / implications of the presented work and/or its intended application is expected where appropriate. This includes an acknowledgment of ethical considerations for papers that include human-subjects research.

Length and formatting

The maximum length is 14 pages (excluding references) using the template indicated below (new ACM single column format). We encourage papers of any length up to 14 pages; reviewers will be asked to comment on whether the length is appropriate for the contribution. Shorter papers should generally report on advances that can be described, set into context, and evaluated concisely; they are not “work-in-progress” reports but rather complete reports on a smaller or simpler-to-describe but complete research work. Longer papers should reflect more complex innovations or studies and should have a thorough discussion of related work. Appendices count toward the page limit—we recommend that supplementary material is linked to an external source using an anonymized URL. 

Each accepted paper will be included in the conference proceedings and presented at the conference. 

Papers must be formatted as a single-column manuscript according to the new workflow for ACM publications. The templates and instructions are available here: https://www.acm.org/publications/taps/word-template-workflow.

Available templates include:

Note: Accepted papers will be subject to a further revision to meet the requirements of camera-ready format required by ACM. We strongly recommend the usage of LaTeX/Overleaf for the camera-ready papers to minimize the extent of reformatting. Users of the Word template must use a recent version Microsoft Word(Windows: Word 2007 or above, Mac: Word 2011 or above; other formats such as Open Office, etc., are not admitted) for the camera-ready submission to avoid incompatibility issues. Instructions for the preparation of the camera-ready versions of accepted papers will be provided after acceptance. This might include instructions to prepare a video of the accepted contribution.

Authors are strongly encouraged to provide “alt text” (alternative text) for floats (images, tables, etc.) in their content so that readers with disabilities can be given descriptive information for these floats that are important to the work. The descriptive text will be displayed in place of a float if the float cannot be loaded. This benefits the author as well as it broadens the reader base for the author’s work. Moreover, the alt text provides in-depth float descriptions to search engine crawlers, which helps to properly index these floats. Additionally, authors should follow the ACM Accessibility Recommendations for Publishing in Color and SIG ACCESS guidelines on describing figures.

Should you have any questions or issues going through the instructions above, please contact support at acmtexsupport@aptaracorp.com for both LaTeX and Microsoft Word inquiries.

Accepted papers will be later submitted to ACM’s new production platform where authors will be able to review PDF and HTML output formats before publication.

UMAP uses a double blind review process. Authors must omit their names and affiliations from submissions, and avoid obvious identifying statements. For instance, citations to the authors’ own prior work should be made in the third person. Failure to anonymize your submission results in the desk-rejection of your paper.

Ethical Review and Human-Subjects Research Considerations

UMAP expects papers to include a discussion of the ethical considerations behind / implications of the presented work and/or its intended application where appropriate. UMAP further expects all authors to comply with ethical standards and regulatory guidelines associated with human subjects research, including research involving human participants and research using personally identifiable data. Papers reporting on such human subjects research must include a statement identifying any regulatory review the research is subject to (and identifying the form of approval provided), or explaining the lack of required review. 

The ACM Code of Ethics gives the UMAP program committee the right to (desk-)reject papers that perpetuate harmful stereotypes, employ unethical research practices, or uncritically present outcomes/implications that clearly disadvantage minoritized communities. Reviewers will be asked to consider whether the research was conducted in compliance with professional ethical standards and applicable regulatory guidelines.

Program Chairs

  • Liliana Ardissono, University of Torino, Italy
  • Bart Knijnenburg, Clemson University, US
  • Contact: umap2022-program@um.org