Authors can submit their papers through Easy Chair at the link: https://easychair.org/conferences/?conf=gmap2026. See important dates for deadlines.
TOPICS
We seek contributions in topics including, but not limited to:
Group Recommender Systems and Group Modeling.
Going beyond standard aggregation strategies, by incorporating adaptable user and group profiles as the group advances towards their choice.
How to ensure that preferences of each user (group member) are appropriately taken into consideration when creating group recommendations?
How to adapt such recommendations to the user's changing preferences, if any?
What are the appropriate aggregation methods for user profiles given a contextual situation setting?
Group decision-making process support.
How can we best help groups reach acceptable joint decisions, when the system supports discussion or several rounds of suggestions?
How to decide the appropriate time and format of the suggestion?
How large language models can be used to support the group decision-making process?
Adaptive team formation systems.
How to design systems and algorithms that adapt their team formation decisions to the involved users' suggestions?
How to ascertain that workers can efficiently explore the very large space of potential collaborators, and form work alliances that are optimal for themselves but also for each (creative) task?
How to develop modeling approaches that afford users personal liberty and flexibility (e.g. in selecting their teammates), while maintaining appropriate collaboration and work conduct standards (e.g. in avoiding workplace discrimination)?
Multi-stakeholder and Multi-objective Scenarios as Heterogeneous Groups.
Approaches aiming to bridge the realms of multi-stakeholder and group-recommending scenarios. This may include both applying multi-stakeholder paradigms to traditional group recommendation tasks and vice versa.
How to design methods capable of dealing with heterogeneous groups, possibly including different quality criteria or non-trivial hierarchy, checks, and balances?
Explainability.
How can the reasoning of a Group Recommender System be made more transparent and interpretable to group members, so that the trust in a system as well as willingness to accept recommendations is increased?
How large language models can be applied to improve the explainability of a GRSys?
How to assist online crowd workers to interpret the team formation algorithm's decisions? How to assist them comprehend the impact that their own input to the algorithm has, in the context of several decision interconnections?
Adaptability.
When a GRSys serves as a decision-support tool within a conversational system, how can this system be made more adaptable to (i) group dynamics, (ii) contextual situations, (iii) moods and emotions in the group, (iv) desired privacy levels, (v) diversity and specificity of preferences in different "sub-domains", etc.?
Privacy.
What are the limitations of existing approaches for implementing privacy in GRSys, and how to overcome these limitations? What are the trade-offs that must be made between privacy and explainability?
Fairness.
What are the potential trade-offs between fairness and other performance metrics, such as accuracy and personalization? How can these trade-offs be balanced to improve the overall performance?
Evaluation.
How to address the limitations in existing data sets?
How to define a "valid" set of baselines considering different dimensions and features of a Group Recommender System being evaluated?
How to define a well-generalising evaluation framework that covers particularities of various GRSSys goals that will yield reproducible outputs?
What are the best practices to adopt while evaluating GRSys online?
LLM Applications.
How can we employ LLMs to obtain a natural interaction with group members allowing effective and user-friendly preference elicitation?
How can we use LLMs to produce privacy-preserving explanations?
How can LLMs help in adapting recommendations to specific contexts, considering feedback received from users?
SUBMISSIONS
We encourage the submission of original and novel contributions in the form of full papers, short papers and work-in-progress, or system demonstrations.
Page Limits
Long Papers (12 content pages + references);
Short Papers (6 content pages + references);
Demonstrations (4 content pages + references);
Note that all content except for references (including figures, tables, proofs, appendixes, acknowledgments, etc.) counts towards the page limit.
Submission site
All submitted papers will be evaluated by at least two members of the program committee, based on originality, significance, relevance and technical quality. The review process will be single-blind (Authors' names and affiliations can be included in the submission).
Template
Submissions should follow the CEURART template available for Overleaf (https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw), LaTeX (http://ceur-ws.org/Vol-XXX/CEURART.zip), or LibreOffice (https://ceur-ws.org/Vol-XXX/CEUR-Template-1col.odt).
Accessibility
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 and 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.
Proceedings and registration
All accepted papers shall be published by CEUR-WS within the dedicated proceedings containing all UMAP 2026 workshops. At least one author of each accepted paper must register for the workshop and present the paper there.