Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation

• URL: http://arxiv.org/abs/2407.17234v2
• Date: Sun, 28 Jul 2024 15:00:40 GMT
• Title: Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
• Abstract summary: We propose an innovative framework termed Intent-guided Heterogeneous Graph Contrastive Learning (IHGCL) IHGCL aims to enhance CL-based recommendation by capturing intents contained within meta-paths. Empirical evaluations conducted across six distinct datasets demonstrate the superior performance of our IHGCL framework.
• Score: 17.889906784627904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive Learning (CL)-based recommender systems have gained prominence in the context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of representations across different views. However, existing frameworks often neglect the fact that user-item interactions within HG are governed by diverse latent intents (e.g., brand preferences or demographic characteristics of item audiences), which are pivotal in capturing fine-grained relations. The exploration of these underlying intents, particularly through the lens of meta-paths in HGs, presents us with two principal challenges: i) How to integrate CL with intents; ii) How to mitigate noise from meta-path-driven intents. To address these challenges, we propose an innovative framework termed Intent-guided Heterogeneous Graph Contrastive Learning (IHGCL), which designed to enhance CL-based recommendation by capturing the intents contained within meta-paths. Specifically, the IHGCL framework includes: i) a meta-path-based Dual Contrastive Learning (DCL) approach to effectively integrate intents into the recommendation, constructing intent-intent contrast and intent-interaction contrast; ii) a Bottlenecked AutoEncoder (BAE) that combines mask propagation with the information bottleneck principle to significantly reduce noise perturbations introduced by meta-paths. Empirical evaluations conducted across six distinct datasets demonstrate the superior performance of our IHGCL framework relative to conventional baseline methods. Our model implementation is available at https://github.com/wangyu0627/IHGCL.

Related papers

• Collaborative Memory Augmentation for Generative Recommendation [49.76630658239287]
  We propose textbfOMEGA, a cOllaborative MEmory augmentation framework for Generative recommendAtion.<n>We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations.<n>These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns.
  arXiv  Detail & Related papers  (2026-08-02T15:34:43Z)
• Bilateral Intent-Enhanced Sequential Recommendation with Embedding Perturbation-Based Contrastive Learning [1.7522809149611367]
  Bilateral Intent-enhanced Embedding Perturbation-based Contrastive Learning framework.<n>BIPCL integrates multi-intent signals into both item and sequence representations via a bilateral intent-enhancement mechanism.<n> experiments on benchmark datasets demonstrate that BIPCL consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-04-03T07:55:04Z)
• GoViG: Goal-Conditioned Visual Navigation Instruction Generation [69.79110149746506]
  We introduce Goal-Conditioned Visual Navigation Instruction Generation (GoViG), a new task that aims to autonomously generate precise and contextually coherent navigation instructions.<n>GoViG exclusively leverages raw egocentric visual data, substantially improving its adaptability to unseen and unstructured environments.
  arXiv  Detail & Related papers  (2025-08-13T07:05:17Z)
• Str-GCL: Structural Commonsense Driven Graph Contrastive Learning [12.717666156052342]
  We propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL)<n>Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework.<n>To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL.
  arXiv  Detail & Related papers  (2025-07-09T03:41:48Z)
• Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering [16.02820746003461]
  graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems. In this paper, we reveal via theoretical derivation that the gradient descent process of the CL objective is formally equivalent to graph convolution. We propose a novel neighborhood aggregation objective to bring users closer to all interacted items while pushing them away from other positive pairs.
  arXiv  Detail & Related papers  (2025-04-14T11:22:41Z)
• LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs [22.322402072526927]
  Heterogeneous Graph Contrastive Learning (HGCL) usually requires pre-defined meta-paths. textsfLAMP integrates various meta-path sub-graphs into a unified and stable structure. textsfLAMP significantly outperforms existing state-of-the-art unsupervised models in terms of accuracy and robustness.
  arXiv  Detail & Related papers  (2024-09-10T08:27:39Z)
• Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation [84.45144851024257]
  CoGCL aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes. We introduce a multi-level vector quantizer in an end-to-end manner to quantize user and item representations into discrete codes. For neighborhood structure, we propose virtual neighbor augmentation by treating discrete codes as virtual neighbors. Regarding semantic relevance, we identify similar users/items based on shared discrete codes and interaction targets to generate the semantically relevant view.
  arXiv  Detail & Related papers  (2024-09-09T14:04:17Z)
• High-Order Fusion Graph Contrastive Learning for Recommendation [16.02820746003461]
  Graph contrastive learning (GCL)-based methods typically implement CL by creating contrastive views through various data augmentation techniques. Existing CL-based methods use traditional CL objectives to capture self-supervised signals. We propose a High-order Fusion Graph Contrastive Learning (HFGCL) framework for recommendation.
  arXiv  Detail & Related papers  (2024-07-29T04:30:38Z)
• Homophily-Driven Sanitation View for Robust Graph Contrastive Learning [28.978770069310276]
  We investigate adversarial robustness of unsupervised Graph Contrastive Learning (GCL) against structural attacks. We present a robust GCL framework that integrates a homophily-driven sanitation view, which can be learned jointly with contrastive learning. We conduct extensive experiments to evaluate the performance of our proposed model, GCHS, against two state of the art structural attacks on GCL.
  arXiv  Detail & Related papers  (2023-07-24T06:41:59Z)
• Similarity Preserving Adversarial Graph Contrastive Learning [5.671825576834061]
  We propose a similarity-preserving adversarial graph contrastive learning framework. In this paper, we show that SP-AGCL achieves a competitive performance on several downstream tasks.
  arXiv  Detail & Related papers  (2023-06-24T04:02:50Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• Unifying Graph Contrastive Learning with Flexible Contextual Scopes [57.86762576319638]
  We present a self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short) Our algorithm builds flexible contextual representations with contextual scopes by controlling the power of an adjacency matrix. Based on representations from both local and contextual scopes, distL optimises a very simple contrastive loss function for graph representation learning.
  arXiv  Detail & Related papers  (2022-10-17T07:16:17Z)
• Heterogeneous Graph Contrastive Multi-view Learning [11.489983916543805]
  Graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. We propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets.
  arXiv  Detail & Related papers  (2022-10-01T10:53:48Z)
• Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation [62.96628432641806]
  Scene Graph Generation aims to first encode the visual contents within the given image and then parse them into a compact summary graph. We first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder.
  arXiv  Detail & Related papers  (2022-03-18T09:14:13Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)

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.