ACM UMAP 2020 – 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 ACM and will be part of the ACM Digital Library.

We welcome submissions related to user modeling, personalization and adaptation in any area. The topics and subtopics listed below are not intended to limit possible contributions. The theme for UMAP 2020 will be “Responsible Personalization.” Submissions in all areas that emphasize ethical dimensions of personalized systems are welcome.


Important Dates

  • Abstract: January 31, 2020
  • Full/Short paper: February 7, 2020 (extended for submitted abstracts to Feb. 9)
  • Notification: March 27, 2020
  • Camera-ready: May 3, 2020

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

Conference Topics

Personalized Recommender Systems

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. These systems assist users 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 user 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 topic 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 multistakeholder environments
  • Explanations and trust
  • Context-aware recommendation algorithms
  • Recommending to groups of users
  • Case studies of real-world implementations
  • Novel, psychologically-informed user- and item-modeling
  • Responsible recommendation including fairness and transparency
  • Music and media recommendation

Adaptive Hypermedia and the Semantic Web

Adaptive hypermedia and adaptive web explore alternatives to the traditional “one-size-fits-all” approach in the development of web and hypermedia systems. Typically, these 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. However, this area has recently expanded towards complementary research paths in order to improve the interaction with the user. For instance, it has recently acquired methods from the interactive support to information search to enhance the user control during information exploration, with the objective of improving information access by fusing automatic and human capabilities; moreover, it is expanding towards multimodal user interfaces and Augmented Reality to provide users with richer types of content and to support a more engaging fruition of information.
The 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. UMAP 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 the 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
  •  Advanced user interfaces, such as VR and AR, for Adaptive Hypermedia

Intelligent User Interfaces

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 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):

  • Adaptive personal virtual assistants (e.g., interaction with social 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

Personalized Social Web

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

Technology-Enhanced Adaptive Learning

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. Learning is a complex human process that involves cognitive, metacognitive, motivational, affective and psychomotor aspects that 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.
This topic 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.

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
  • Learning analytics

Privacy, Fairness and Transparency

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. Privacy topics include the management of the tradeoff between data collection and user modeling as well as innovative means to resolve privacy problems through algorithms, interfaces, or other technical or non-technical means. Fairness in user modeling includes the spectrum from algorithmic fairness to social implications of adaptive systems.  We define transparency both in terms of system usability and as a means to resolve problems with privacy and fairness.
This topic is particularly important to the UMAP 2020 theme of “Responsible Personalization” and we therefore we encourage authors to submit any work that ascribes to or advances the general idea of “adaptive systems that care”.

Privacy topics:

  • Analysis of privacy implications of user modeling
  • Algorithmic, architectural and interactive solutions to privacy, including personalized solutions
  • Usable privacy for adaptive systems
  • User perceptions of privacy in UMAP applications
  • Studies of users’ privacy-related behaviors in UMAP applications
  • Privacy measurement, prediction and personalization
  • Privacy education for user modeling
  • Modeling of data protection and privacy requirements including the economics of privacy and personal data

Fairness topics:

  • Ethical considerations for user modeling including ‘filter bubble’ or ‘balkanization’ effects
  • UMAP applications for underrepresented groups
  • Bias and discrimination in user modeling, including imbalance in meeting the needs of different groups of users
  • Balancing needs of users versus system owners and others, including the ethics of explore/exploit strategies or A/B testing
  • Enhancing/embracing diversity and cultural differences in user modeling
  • Algorithmic methods for measuring and increasing fairness
  • User perceptions of fairness

Transparency topics:

  • User perceptions of transparency
  • Interface and algorithmic innovations that increase transparency
  • Explanations and visualizations for transparency
  • (User-centric) evaluations of methods that increase transparency
  • Measuring transparency

Personalized Health

Growing health issues and rising treatment costs mean that technological systems are increasingly important for global health. Personalized systemstailored to the needs and behaviors of individual patients, are one of the promising approaches to health promotion by encouraging lifestyle change, managing treatment programs 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.

This topic  invites scholars working in these or related areas to contribute to the discourse on how technology can promote health, and encourage contributions from 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 for health
  • Medical privacy, patient trust, and biomedical ethics as applied in user modeling contexts

Theory, Opinion, Reflection

Theory, Opinion and Reflection (TOR) are position papers that look critically at ongoing and emerging research topics, reflections on important trends in the field and blue sky future ideas for UMAP research. They offer an opportunity for discussing thought-provoking work relevant to the UMAP community, albeit they are not yet ready to be published as a full-length research papers at a refereed conference. They should be of sufficient quality and relevance to the UMAP community and demonstrate the ability to spur discussion and debate on the future course of the UMAP research.
Note: TOR papers will only be accepted as short papers.

Submission and Review Process

Papers should be submitted through EasyChair:


The ACM User Modeling, Adaptation, and Personalization (ACM UMAP) 2020 Conference will include high quality peer-reviewed papers any area of user modeling, adaptation and personalization. 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.

Long (8 pages + references) and Short (4 pages + references) papers in ACM style. Original research papers addressing the theory and/or 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 its 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 may be revised and resubmitted as posters.

Instructions for Authors

Papers must be formatted using the ACM SIG Standard (SIGCONF) 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.

Papers will be reviewed single-blind and do not need to be anonymized before submission.

Proceedings and Registration Policy

All accepted papers will be published by ACM and will be available via 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. Student registration fee is allowed to students who present a student paper.

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 the conference. The official publication date affects the deadline for any patent filings related to published work.