Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation

• URL: http://arxiv.org/abs/2605.08499v1
• Date: Fri, 08 May 2026 21:28:27 GMT
• Title: Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation
• Abstract summary: We propose a multi-view graph contrastive learning framework.<n>The proposed method enhances user representations through multi-view knowledge graph distillation.<n>We design a self-supervised contrastive learning module that performs comparisons across three perspectives.
• Score: 6.7681468436772585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the use of edge information provided by knowledge graphs together with the advantages of higher-order connectivity in graph neural networks for recommendation systems has become an important research direction. However, existing approaches are often limited by sparse labels, insufficient graph structure learning, and noisy entities in the knowledge graph, which reduce recommendation accuracy. To address these limitations, we propose a multi-view graph contrastive learning framework. The proposed method enhances user representations through multi-view knowledge graph distillation, enabling more accurate modeling of user preferences over entities and relations. The network aggregates neighborhood entity information to construct informative item representations. Furthermore, we design a multi-level self-supervised contrastive learning module that performs comparisons across three perspectives: Inter-Level, Intra-Level, and Interaction-Level. This design improves the model's ability to generalize across intra-class samples while increasing discrimination between inter-class samples, thereby enabling more effective multi-dimensional feature modeling. We conduct extensive experiments on three public datasets using both baseline and ablation settings. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods. Ablation studies further verify the effectiveness of each module in the proposed model.

Related papers

• Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems [2.620825811168925]
  This paper implements an explainable recommendation model that integrates knowledge graphs with structure-aware attention mechanisms.<n>The model is built on graph neural networks and incorporates a multi-hop neighbor aggregation strategy.<n> Experiments conducted on the Amazon Books dataset validate the superior performance of the proposed model.
  arXiv  Detail & Related papers  (2025-10-11T08:39:34Z)
• Knowledge graph-based personalized multimodal recommendation fusion framework [8.468510273008393]
  Cross-Graph Cross-Modal Mutual Information-Driven Unified Knowledge Graph Learning and Recommendation Framework (CrossGMMI-DUKGLR)<n>This paper reviews existing knowledge graph recommendation frameworks, identifying shortcomings in modal interaction and higher-order dependency modeling.<n>We propose the Cross-Graph Cross-Modal Mutual Information-Driven Unified Knowledge Graph Learning and Recommendation Framework (CrossGMMI-DUKGLR)
  arXiv  Detail & Related papers  (2025-09-03T02:17:28Z)
• Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning [51.0864247376786]
  We introduce a Knowledge Graph Enhanced Generative Multi-modal model (KG-GMM) that builds an evolving knowledge graph throughout the learning process.<n>During testing, we propose a Knowledge Graph Augmented Inference method that locates specific categories by analyzing relationships within the generated text.
  arXiv  Detail & Related papers  (2025-03-24T07:20:43Z)
• Dynamic Knowledge Selector and Evaluator for recommendation with Knowledge Graph [4.95829270467941]
  We propose a dynamic knowledge-selecting and evaluating method guided by collaborative signals to distill information in the knowledge graph.<n> Specifically, we use a Chain Route Evaluator to evaluate the contributions of different neighborhoods for the recommendation task.
  arXiv  Detail & Related papers  (2025-02-21T17:51:37Z)
• Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation [84.45144851024257]
  We propose a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes.<n>The core idea is to map users and items into discrete codes rich in collaborative information for reliable and informative contrastive view generation.
  arXiv  Detail & Related papers  (2024-09-09T14:04:17Z)
• Visual Commonsense based Heterogeneous Graph Contrastive Learning [79.22206720896664]
  We propose a heterogeneous graph contrastive learning method to better finish the visual reasoning task. Our method is designed as a plug-and-play way, so that it can be quickly and easily combined with a wide range of representative methods.
  arXiv  Detail & Related papers  (2023-11-11T12:01:18Z)
• Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data [52.491074276133325]
  Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. We propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
  arXiv  Detail & Related papers  (2022-11-08T09:19:32Z)
• SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction [28.87290783250351]
  Link prediction is the task of inferring missing links between entities in knowledge graphs. We propose a novel Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction.
  arXiv  Detail & Related papers  (2022-10-10T17:40:19Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)
• Deep Contrastive Learning for Multi-View Network Embedding [20.035449838566503]
  Multi-view network embedding aims at projecting nodes in the network to low-dimensional vectors. Most contrastive learning-based methods mostly rely on high-quality graph embedding. We design a novel node-to-node Contrastive learning framework for Multi-view network Embedding (CREME)
  arXiv  Detail & Related papers  (2021-08-16T06:29:18Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)

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.