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Widening the Role of Group Recommender Systems with CAJO

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arxiv 2504.05934 v1 pith:BRC5TTDR submitted 2025-04-08 cs.IR cs.HC

classification cs.IRcs.HC
keywords groupsystemsrecommendercajogrssaiminganalysisapplication
verification ladder T0 review T1 audit T2 compute T3 formal
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Group Recommender Systems (GRSs) have been studied and developed for more than twenty years. However, their application and usage has not grown. They can even be labeled as failures, if compared to the very successful and common recommender systems (RSs) used on all the major ecommerce and social platforms. As a result, the RSs that we all use now, are only targeted for individual users, aiming at choosing an item exclusively for themselves; no choice support is provided to groups trying to select a service, a product, an experience, a person, serving equally well all the group members. In this opinion article we discuss why the success of group recommender systems is lagging and we propose a research program unfolding on the analysis and development of new forms of collaboration between humans and intelligent systems. We define a set of roles, named CAJO, that GRSs should play in order to become more useful tools for group decision making.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support

    cs.IR 2025-07 conditional novelty 5.0 of 10

    The paper proposes reorienting group recommender systems from one-shot preference aggregation to chat-based, agentic decision support powered by large language models.

  2. Point of Interest Recommendation: Pitfalls and Viable Solutions

    cs.IR 2025-07 accept novelty 4.0 of 10

    A reflection paper argues that POI recommendation research is held back by 20 systemic pitfalls in datasets, algorithms, and evaluation, and outlines six solutions.

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