Consensus-aware Contrastive Learning for Group Recommendation

• URL: http://arxiv.org/abs/2504.13703v1
• Date: Fri, 18 Apr 2025 14:03:40 GMT
• Title: Consensus-aware Contrastive Learning for Group Recommendation
• Authors: Soyoung Kim, Dongjun Lee, Jaekwang Kim,
• Abstract summary: Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences.<n>We introduce a Consensus-aware Contrastive Learning for Group Recommendation (CoCoRec) that models group consensus through contrastive learning.<n>Experiments conducted on four benchmark datasets show that CoCoRec consistently outperforms state-of-the-art baselines in both individual and group recommendation scenarios.
• Score: 12.743275229282922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences. A fundamental challenge in this task is deriving a consensus that adequately represents the diverse interests of individual group members. Despite advancements made by deep learning-based models, existing approaches still struggle in two main areas: (1) Capturing consensus in small-group settings, which are more prevalent in real-world applications, and (2) Balancing individual preferences with overall group performance, particularly in hypergraph-based methods that tend to emphasize group accuracy at the expense of personalization. To address these challenges, we introduce a Consensus-aware Contrastive Learning for Group Recommendation (CoCoRec) that models group consensus through contrastive learning. CoCoRec utilizes a transformer encoder to jointly learn user and group representations, enabling richer modeling of intra-group dynamics. Additionally, the contrastive objective helps reduce overfitting from high-frequency user interactions, leading to more robust and representative group embeddings. Experiments conducted on four benchmark datasets show that CoCoRec consistently outperforms state-of-the-art baselines in both individual and group recommendation scenarios, highlighting the effectiveness of consensus-aware contrastive learning in group recommendation tasks.

Related papers

• Leveraging the Dynamics of Leadership in Group Recommendation Systems [0.11510009152620665]
  We introduce a novel approach to group recommendation, with a specific focus on small groups sharing common interests.<n>We propose a recommendation algorithm that emphasizes the dynamics of relationships and trust within the group.<n>This interaction-focused framework ultimately seeks to enhance overall group satisfaction with the recommended choices.
  arXiv  Detail & Related papers  (2025-03-17T07:16:22Z)
• Online Clustering of Dueling Bandits [59.09590979404303]
  We introduce the first "clustering of dueling bandit algorithms" to enable collaborative decision-making based on preference feedback.<n>We propose two novel algorithms: (1) Clustering of Linear Dueling Bandits (COLDB) which models the user reward functions as linear functions of the context vectors, and (2) Clustering of Neural Dueling Bandits (CONDB) which uses a neural network to model complex, non-linear user reward functions.
  arXiv  Detail & Related papers  (2025-02-04T07:55:41Z)
• AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation [7.8148534870343225]
  Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. We propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making.
  arXiv  Detail & Related papers  (2024-09-04T10:03:09Z)
• Learning by Sorting: Self-supervised Learning with Group Ordering Constraints [75.89238437237445]
  This paper proposes a new variation of the contrastive learning objective, Group Ordering Constraints (GroCo) It exploits the idea of sorting the distances of positive and negative pairs and computing the respective loss based on how many positive pairs have a larger distance than the negative pairs, and thus are not ordered correctly. We evaluate the proposed formulation on various self-supervised learning benchmarks and show that it not only leads to improved results compared to vanilla contrastive learning but also shows competitive performance to comparable methods in linear probing and outperforms current methods in k-NN performance.
  arXiv  Detail & Related papers  (2023-01-05T11:17:55Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning [68.45370492516531]
  We introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the Recommender Systems (RS) setting. SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
  arXiv  Detail & Related papers  (2021-10-28T13:22:45Z)
• Focus on the Common Good: Group Distributional Robustness Follows [47.62596240492509]
  This paper proposes a new and simple algorithm that explicitly encourages learning of features that are shared across various groups. While Group-DRO focuses on groups with worst regularized loss, focusing instead, on groups that enable better performance even on other groups, could lead to learning of shared/common features.
  arXiv  Detail & Related papers  (2021-10-06T09:47:41Z)
• Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation [35.841350982832545]
  Group recommendation suffers seriously from the problem of data sparsity. We propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals.
  arXiv  Detail & Related papers  (2021-09-09T12:19:49Z)
• Group Collaborative Learning for Co-Salient Object Detection [152.67721740487937]
  We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms) Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our simple GCoNet outperforms 10 cutting-edge models and achieves the new state-of-the-art.
  arXiv  Detail & Related papers  (2021-03-15T13:16:03Z)
• 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)
• GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation [24.677145454396822]
  We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. We propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group.
  arXiv  Detail & Related papers  (2020-06-05T23:18:19Z)

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.