-------- Forwarded Message -------- Subject: [AISWorld] UMAP ’23: 31st ACM Conference on User Modeling, Adaptation and Personalization: First Call for Papers Date: Tue, 1 Nov 2022 08:33:58 +0200 From: George A. Papadopoulos george@cs.ucy.ac.cy To: aisworld@lists.aisnet.org

*** First Call for Papers ***

UMAP ’23: 31st ACM Conference on User Modeling, Adaptation and Personalization

June 26 - 29, 2023, St. Raphael Resort, Limassol, Cyprus

https://www.um.org/umap2023/

ACM UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or groups of users, and that collect, represent, and model user information. ACM UMAP  is sponsored by ACM SIGCHI and SIGWEB. User Modeling Inc., as the core Steering Committee, oversees the conference organization. The proceedings, published by ACM, will be part of the ACM Digital Library.

The theme of UMAP 2023 is "Personalization in Times of Crisis”. Specifically, we welcome submissions that highlight the impact that critical periods (such as the COVID-19 pandemic, ongoing wars, and climate change, to name a few) can have on user modeling, personalization, and adaptation of (intelligent) systems; the focus is on investigations that capture how these trying times may have influenced user behavior and whether new models are required.

While we encourage submissions related to this theme, the scope of the conference is not limited to the theme only. As always, contributions from academia, industry, and other organizations discussing open challenges or novel research approaches are expected to be supported by rigorous evidence appropriate to the claims (e.g., user study, system evaluation, computational analysis).

Important Dates

• Paper Abstracts: January 19, 2023 (mandatory) • Full paper: January 26, 2023 • Notification: April 11, 2023 • Camera-ready: May 2, 2023 • Conference: June 26 - 29, 2023 Note: The submissions deadlines are at 11:59 pm AoE time (Anywhere on Earth)

Conference Topics

We welcome submissions related to user modeling, personalization, and adaptation of (intelligent) systems targeting a broad range of users and domains. For detailed descriptions and the suggested topics for each track please visit the UMAP 2023 website.

Personalized Recommender Systems This track invites works from researchers and practitioners on recommender systems. In addition to mature research works addressing technical aspects of recommendations, we welcome research contributions that address questions related to user perception, decision-making, and the business value of recommender systems.

Knowledge Graphs, Semantics, Social and Adaptive Web This track welcomes works focused on the use of knowledge representations (i.e., novel knowledge bases), graph algorithms (i.e., graph embedding techniques), and social network analysis at the service of addressing all aspects of personalization, user model building, and personal experience in online social systems. Moreover, this track invites works in adaptive hypermedia, as well as semantic and social web.

Intelligent User Interfaces This track invites works exploring how to make the interaction between computers and people smarter and more productive, leveraging solutions from human-computer interaction, data mining, natural language processing, information visualization, and knowledge representation and reasoning.

Personalizing Learning Experiences through User Modeling This track invites researchers, developers, and practitioners from various disciplines to submit their innovative learning solutions, share acquired experiences, and discuss their modeling challenges for personalized adaptive learning.

Responsibility, Compliance, and Ethics Researchers, developers, and practitioners have a social responsibility to account for the impact that technologies have on individuals (users, providers, and other stakeholders) and society. This track invites works related to the science of building, maintaining, evaluating, and studying adaptive systems that are fair, transparent, respectful of users’ privacy, and beneficial to society.

Personalization for Persuasive and Behavior Change Systems This track invites submissions focused on personalization and tailoring for persuasive technologies, including but not limited to personalization models, user models, computational personalization, design, and evaluation methods. It also welcomes work that brings attention to the user experience and designing personalized and adaptive behavior change technologies.

Virtual Assistants, Conversational Interactions, and Personalized Human-robot Interaction This track invites works investigating new models and techniques for adapting synthetic companions (e.g., virtual assistants, chatbots, social robots) to individual users. With the conversational modality so in vogue across disciplines, this track welcomes work highlighting the model and deployment of synthetic companions driven by conversational search and recommendation paradigms.

Research Methods and Reproducibility This track invites submissions on methodologies to evaluate personalized systems, benchmarks, and measurement scales, with particular attention to the reproducibility of results and techniques. Furthermore, the track looks for submissions that report new insights from reproducing existing works.

Submission and Review Process

Submissions for any of the aforementioned tracks should have a maximum length of *14 pages* (excluding references) in the ACM new single-column format (https://www.acm.org/publications/proceedings-template). (Papers of any length up to 14 pages are encouraged; reviewers will comment on whether the size is appropriate for the contribution.)  Additional review criteria and submission link will be available shortly on the conference website: https://www.um.org/umap2023/%C2%A0.

Accepted papers will be included in the conference proceedings and presented at the conference. At least one author should register for the conference by the early registration date cut-off.

UMAP uses a *double-blind* review process. Authors must omit their names and affiliations from their submissions; they should also avoid obvious identifying statements. For instance, citations to the authors' prior work should be in the third person. Submissions not abiding by anonymity requirements will be desk rejected.

UMAP has a *no dual submission* policy, which is why full paper submissions should not be currently under review at another publication venue. Further, UMAP operates under the ACM Conference Code of Conduct (https://www.acm.org/about-acm/policy-against-harassment).

Program Chairs

• Julia Neidhardt, TU Wien, Austria • Sole Pera, TU Delft, The Netherlands

Track Chairs

Personalized Recommender Systems • Noemi Mauro (University of Torino, Italy) • Olfa Nasraoui (University of Louisville, USA) • Marko Tkalcic (University of Primorska, Slovenia)

Knowledge Graphs, Semantics, Social and Adaptive Web • Daniela Godoy (ISISTAN - CONICET/UNICEN University, Argentina) • Cataldo Musto (University of Bari, Italy)

Intelligent User Interfaces • Bart Knijnenburg (Clemson University, USA) • Katrien Verbert (KU Leuven, Belgium) • Wolfgang Wörndl (TU Munich, Germany)

Personalizing Learning Experiences through User Modeling Oleksandra Poquet (TU Munich, Germany) • Olga C. Santos (UNED, Spain)

Responsibility, Compliance, and Ethics • Michael Ekstrand (Boise State University, USA) • Peter Knees (TU Wien, Austria)

Personalization for Persuasive and Behavior Change Systems • Federica Cena (University of Torino, Italy) • Rita Orji (Dalhousie University, Canada) • Jun Zhao (Oxford University, England)

Virtual Assistants, Conversational Interactions, and Personalized Human-robot Interaction • Li Chen (Hong Kong Baptist University, Hong Kong) • Yi Zhang (University of California Santa Cruz, USA) • Ingrid Zukerman (Monash University, Australia)

Research Methods and Reproducibility • Dietmar Jannach (University of Klagenfurt, Austria) • Alan Said (University of Gothenburg, Sweden)

Contact information: umap2023-program@um.org _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org