Widening the Role of Group Recommender Systems with CAJO

• URL: http://arxiv.org/abs/2504.05934v1
• Date: Tue, 08 Apr 2025 11:47:40 GMT
• Title: Widening the Role of Group Recommender Systems with CAJO
• Authors: Francesco Ricci, Amra Delić,
• Abstract summary: Group Recommender Systems (GRSs) have been studied and developed for more than twenty years.<n>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.<n>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.
• Score: 1.9336815376402723
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Towards LLM-Enhanced Group Recommender Systems [39.08078205630303]
  Group recommender systems are designed to generate and explain recommendations for groups.<n>This paper analyzes in which way large language models (LLMs) can support these aspects.
  arXiv  Detail & Related papers  (2025-07-25T13:59:54Z)
• Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support [8.7111059833539]
  We envision group recommender systems where human group members interact in a chat and an AI-based group recommendation agent assists the decision-making process in an agentic way.<n> Ultimately, this shall lead to a more natural group decision-making environment and finally to wider adoption of group recommendation systems in practice.
  arXiv  Detail & Related papers  (2025-07-01T07:56:37Z)
• Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems [65.75265303064654]
  Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users.<n>Large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences.<n>We propose CRAG, Collaborative Retrieval Augmented Generation for LLM-based CRS.
  arXiv  Detail & Related papers  (2025-02-19T22:47:40Z)
• Identify Then Recommend: Towards Unsupervised Group Recommendation [13.238029824207151]
  Group Recommendation (GR) aims to recommend items to groups of users. We present a novel unsupervised group recommendation framework named underlineIdentify underlineThen underlineRecommend (underlineITR) We deploy ITR on the industrial recommender and achieve promising results.
  arXiv  Detail & Related papers  (2024-10-31T09:24:22Z)
• A Multi-Agent Conversational Recommender System [47.49330334611104]
  We propose Multi-Agent Conversational Recommender System (MACRS) First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents. Second, we propose a user feedback-aware reflection mechanism which leverages user feedback to reason errors made in previous turns to adjust the dialogue act planning.
  arXiv  Detail & Related papers  (2024-02-02T04:20:13Z)
• A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems [13.89038866451741]
  We propose CP-FairRank, an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side. We demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising overall recommendation quality.
  arXiv  Detail & Related papers  (2024-02-01T10:42:05Z)
• RecRec: Algorithmic Recourse for Recommender Systems [41.97186998947909]
  It is crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. We propose a recourse framework for recommender systems, targeted towards the content providers.
  arXiv  Detail & Related papers  (2023-08-28T22:26:50Z)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. In our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• Meta Policy Learning for Cold-Start Conversational Recommendation [71.13044166814186]
  We study CRS policy learning for cold-start users via meta reinforcement learning. To facilitate policy adaptation, we design three synergetic components.
  arXiv  Detail & Related papers  (2022-05-24T05:06:52Z)
• Learning Multi-Attention Context Graph for Group-Based Re-Identification [214.84551361855443]
  Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. In this work, we consider employing context information for identifying groups of people, i.e., group re-id. We propose a novel unified framework based on graph neural networks to simultaneously address the group-based re-id tasks.
  arXiv  Detail & Related papers  (2021-04-29T09:57:47Z)
• DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback [0.5584060970507505]
  We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users. reverse social choice aims to infer the preferences of those users involved in observed group decisions.
  arXiv  Detail & Related papers  (2021-03-13T02:05:26Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.