Hypergraph Convolutional Network for Group Recommendation

Jia, Renqi; Zhou, Xiaofei; Dong, Linhua; Pan, Shirui

doi:10.1109/icdm51629.2021.00036

Cited by 32 publications

(4 citation statements)

References 41 publications

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“…• HyperGroup [11]: models the group as hyperedge and proposes a hyperedge embedding-based representation learning method. • HCR [36]: is a dual channel hypergraph convolutional network to capture memberlevel and group-level preferences. Implementation details.…”

Section: Baseline Methodsmentioning

confidence: 99%

GroupMO: a memory-augmented meta-optimized model for group recommendation

Hong,

Yang,

Chao

et al. 2024

World Wide Web

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Group recommendation aims to suggest desired items for a group of users. Existing methods can achieve inspiring results in predicting the group preferences in data-rich groups. However, they could be ineffective in supporting cold-start groups due to their sparsity interactions, which prevents the model from understanding their intent. Although cold-start groups can be alleviated by meta-learning, we cannot apply it by using the same initialization for all groups due to their varying preferences. To tackle this problem, this paper proposes a memory-augmented meta-optimized model for group recommendation, namely GroupMO. Specifically, we adopt a clustering method to assemble the groups with similar profiles into the same cluster and design a representative group profile memory to guide the preliminary initialization of group embedding network for each group by utilizing those clusters. Besides, we also design a group shared preference memory to guide the prediction network initialization at a more refined granularity level for different groups, so that the shared knowledge can be better transferred to groups with similar preferences. Moreover, we incorporate those two memories to optimize the meta-learning process. Finally, extensive experiments on two real-world datasets demonstrate the superiority of our model.

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Section: Baseline Methodsmentioning

confidence: 99%

GroupMO: a memory-augmented meta-optimized model for group recommendation

Hong,

Yang,

Chao

et al. 2024

World Wide Web

View full text Add to dashboard Cite

show abstract

“…Late-stage aggregation involves deriving group-item recommendation scores after the individual preference processing, employing strategies such as the maximum pleasure, least misery and average satisfaction strategies [13,16,23]. These traditional approaches, however, are heavily heuristics dependent, which compromises their adaptability and expressive capabilities [26,27]. Early aggregation, conversely, entails the initial consolidation of individual preferences to formulate a collective group preference, which is subsequently utilized for recommendation generation.…”

Section: Group Recommendationmentioning

confidence: 99%

Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group Recommendation

Gao,

Zhang,

2024

Applied Sciences

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Group recommender systems aim to provide recommendations to a group of users as a whole rather than to individual users. Nonetheless, prevailing methodologies predominantly aggregate user preferences without adequately accounting for the unique individual intents influencing item selection. This oversight becomes particularly problematic in the context of ephemeral groups formed by users with limited shared historical interactions, which exacerbates the data sparsity challenge. In this paper, we introduce a novel Disentangled Self-Attention Group Recommendation framework with auto-regressive contrastive learning method, termed DAGA. This framework not only employs disentangled neural architectures to reconstruct the multi-head self-attention network but also incorporates modules for mutual information optimization via auto-regressive contrastive learning to better leverage the context information of user–item and group–item historical interactions, obtaining group representations and further executing recommendations. Specifically, we develop a disentangled model comprising multiple components to individually assess and interpret the diverse intents of users and their impacts on collective group preferences towards items. Building upon this model, we apply the principle of contrastive mutual information maximization to train our framework, aligning the group representations with the corresponding user representations derived from each factor of the disentangled model, thereby enriching the contextual understanding required to effectively address the challenges posed by ephemeral groups. Empirical evaluations conducted on three real-world benchmark datasets substantiate the superior performance of our proposed framework over existing state-of-the-art group recommendation approaches.

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“…Yang et al [42] treat the vertices and hyperedges equally to solve the symmetric information loss problem of data co-occurrence. Various types of practices [13] based on hypergraphs are also evolving, such as pose estimation [16,29], link prediction [11], recommendation [23,38,39,44], and brain state classification [6,32].…”

Section: Hypergraph Neural Networkmentioning

confidence: 99%

Multi-order hypergraph convolutional networks integrated with self-supervised learning

Huang

Lei

Jiang

et al. 2023

Complex Intell. Syst.

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Hypergraphs, as a powerful representation of information, effectively and naturally depict complex and non-pair-wise relationships in the real world. Hypergraph representation learning is useful for exploring complex relationships implicit in hypergraphs. However, most methods focus on the 1-order neighborhoods and ignore the higher order neighborhood relationships among data on the hypergraph structure. These often result in underutilization of hypergraph structure. In this paper, we exploit the potential of higher order neighborhoods in hypergraphs for representation and propose a Multi-Order Hypergraph Convolutional Network Integrated with Self-supervised Learning. We first encode the multi-channel network of the hypergraph by a high-order spectral convolution operator that captures the multi-order representation of nodes. Then, we introduce an inter-order attention mechanism to preserve the low-order neighborhood information. Finally, to extract valid embedding in the higher order neighborhoods, we incorporate a self-supervised learning strategy based on maximizing mutual information in the multi-order hypergraph convolutional network. Experiments on several hypergraph datasets show that the proposed model is competitive with state-of-the-art baselines, and ablation studies show the effectiveness of higher order neighborhood development, the inter-order attention mechanism, and the self-supervised learning strategy.

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Hypergraph Convolutional Network for Group Recommendation

Cited by 32 publications

References 41 publications

GroupMO: a memory-augmented meta-optimized model for group recommendation

GroupMO: a memory-augmented meta-optimized model for group recommendation

Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group Recommendation

Multi-order hypergraph convolutional networks integrated with self-supervised learning

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