Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation

• URL: http://arxiv.org/abs/2502.09050v1
• Date: Thu, 13 Feb 2025 08:05:14 GMT
• Title: Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation
• Authors: Chae-Hyun Kim, Yoon-Ryung Choi, Jin-Duk Park, Won-Yong Shin,
• Abstract summary: Group-GF is a new approach for extremely fast recommendations of similarity to each group via multi-view graph (GF) We show that Group-GF significantly reduces runtime and achieves state-of-the-art recommendation accuracy.
• Score: 7.787211625411271
• License:
• Abstract: Group recommendation aims at providing optimized recommendations tailored to diverse groups, enabling groups to enjoy appropriate items. On the other hand, most existing group recommendation methods are built upon deep neural network (DNN) architectures designed to capture the intricate relationships between member-level and group-level interactions. While these DNN-based approaches have proven their effectiveness, they require complex and expensive training procedures to incorporate group-level interactions in addition to member-level interactions. To overcome such limitations, we introduce Group-GF, a new approach for extremely fast recommendations of items to each group via multi-view graph filtering (GF) that offers a holistic view of complex member-group dynamics, without the need for costly model training. Specifically, in Group-GF, we first construct three item similarity graphs manifesting different viewpoints for GF. Then, we discover a distinct polynomial graph filter for each similarity graph and judiciously aggregate the three graph filters. Extensive experiments demonstrate the effectiveness of Group-GF in terms of significantly reducing runtime and achieving state-of-the-art recommendation accuracy.

Related papers

• Cluster-based Graph Collaborative Filtering [55.929052969825825]
  Graph Convolution Networks (GCNs) have succeeded in learning user and item representations for recommendation systems. Most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. We propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF)
  arXiv  Detail & Related papers  (2024-04-16T07:05:16Z)
• Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph [68.08590487960475]
  We propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG) We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation layer, which is based on our proposed factorized attention mechanism.
  arXiv  Detail & Related papers  (2022-11-02T01:42:20Z)
• ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering [52.491074276133325]
  We propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering.
  arXiv  Detail & Related papers  (2022-03-01T02:32:25Z)
• Self-supervised Graph Learning for Occasional Group Recommendation [28.337475919795008]
  We study the problem of recommending items to occasional groups (a.k.a. cold-start groups) Due to the extreme sparsity issue of the occasional groups' interactions with items, it is difficult to learn high-quality embeddings for these occasional groups. This paper proposes a self-supervised graph learning paradigm, which jointly trains the backbone GNN model to reconstruct the group/user/item embeddings.
  arXiv  Detail & Related papers  (2021-12-04T08:10:06Z)
• Graph Neural Netwrok with Interaction Pattern for Group Recommendation [1.066048003460524]
  We propose the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation) Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.
  arXiv  Detail & Related papers  (2021-09-21T13:42:46Z)
• 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)
• 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)
• Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks [68.9026534589483]
  RioGNN is a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation.
  arXiv  Detail & Related papers  (2021-04-16T04:30:06Z)
• 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.