SubmissionS

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 each user's recommendation is appropriately taken into consideration when creating group recommendations? 

• How to adapt such recommendations to the user's changing preferences, if any? 

• What is the best aggregation method for user profiles?

Group decision-making process support.

• How can we best help groups to reach their joint decisions, when discussion or several rounds of suggestions are supported by the system?

• 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 the 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)?

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 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 Group Recommender System serves as a decision-support tool within a conversational system, how can this system be made more adaptable to (i) group dynamics, (ii) contextual situation, (iii) moods and emotions in the group, (iv) diversity and specificity of preferences in different “sub-domains”, etc.?

Privacy.

• What are the limitations of existing approaches for implementing privacy in Group Recommender Systems, 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 tackle the lack of appropriate 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 framework that covers particularities of various Group Recommender System goals which will yield reproducible outputs?

SUBMISSION

We encourage the submission of original and novel contributions as (A) Short Papers (till 7 pages + reference - new ACM single column format) or (B) Long Papers (between 8 and 14 pages + reference - new ACM single column format). We recommend that supplementary material is linked to an external source using an anonymized link.

• Submission site: https://easychair.org/conferences/?conf=umap24 (select track GMAP Workshop)

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

Following the ACM Publication Workflow, all authors should submit manuscripts for review in the new ACM single-column format. Instructions for authors are given below:

• LaTeX (use \documentclass[manuscript, review, anonymous]{acmart} in the sample-authordraft.tex file for single-column). Please carefully follow the ACM’s instructions for preparing your article with LaTeX.

• Overleaf (use \documentclass[manuscript,review,anonymous]{acmart} for single-column). Please carefully follow the ACM’s instructions for preparing your article with Overleaf.

• Word. Please carefully follow the ACM’s instructions for preparing your article with Microsoft Word.

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 will be published by ACM within the UMAP 2024 adjunct proceedings and will be available via the ACM Digital Library. At least one author of each accepted paper must register for the workshop and present the paper there.