GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

Federica Lucia Vinella, Amra Delić, Francesco Barile, Ioanna Lykourentzou, Judith Masthoff

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user- and group-centric focus.
Original languageEnglish
Title of host publicationAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
ISBN (Print)9781450392327
Publication statusPublished - 2022
Event30th ACM Conference on User Modeling, Adaptation and Personalization - Barcelona, Spain
Duration: 4 Jul 20227 Jul 2022


Conference30th ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP 2022
Internet address

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