COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation

• URL: http://arxiv.org/abs/2504.04452v1
• Date: Sun, 06 Apr 2025 11:42:49 GMT
• Title: COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation
• Authors: Jinfeng Xu, Zheyu Chen, Wei Wang, Xiping Hu, Sang-Wook Kim, Edith C. H. Ngai,
• Abstract summary: Two key processes in multimodal recommendations are modality fusion and representation learning.<n>We introduce a COmposite grapH convolutional nEtwork with dual-stage fuSION for the multimodal recommendation, named COHESION.
• Score: 26.169114011402232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works in multimodal recommendations, which leverage diverse modal information to address data sparsity and enhance recommendation accuracy, have garnered considerable interest. Two key processes in multimodal recommendations are modality fusion and representation learning. Previous approaches in modality fusion often employ simplistic attentive or pre-defined strategies at early or late stages, failing to effectively handle irrelevant information among modalities. In representation learning, prior research has constructed heterogeneous and homogeneous graph structures encapsulating user-item, user-user, and item-item relationships to better capture user interests and item profiles. Modality fusion and representation learning were considered as two independent processes in previous work. In this paper, we reveal that these two processes are complementary and can support each other. Specifically, powerful representation learning enhances modality fusion, while effective fusion improves representation quality. Stemming from these two processes, we introduce a COmposite grapH convolutional nEtwork with dual-stage fuSION for the multimodal recommendation, named COHESION. Specifically, it introduces a dual-stage fusion strategy to reduce the impact of irrelevant information, refining all modalities using ID embedding in the early stage and fusing their representations at the late stage. It also proposes a composite graph convolutional network that utilizes user-item, user-user, and item-item graphs to extract heterogeneous and homogeneous latent relationships within users and items. Besides, it introduces a novel adaptive optimization to ensure balanced and reasonable representations across modalities. Extensive experiments on three widely used datasets demonstrate the significant superiority of COHESION over various competitive baselines.

Related papers

• Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion [25.140475569677758]
  Multimodal image fusion aims to integrate information from different modalities to obtain a comprehensive image. Existing methods tend to prioritize natural image fusion and focus on information complementary and network training strategies. This paper dissects the significant differences between the two tasks regarding fusion goals, statistical properties, and data distribution.
  arXiv  Detail & Related papers  (2024-11-15T08:36:24Z)
• NativE: Multi-modal Knowledge Graph Completion in the Wild [51.80447197290866]
  We propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities. We construct a new benchmark called WildKGC with five datasets to evaluate our method.
  arXiv  Detail & Related papers  (2024-03-28T03:04:00Z)
• BiVRec: Bidirectional View-based Multimodal Sequential Recommendation [55.87443627659778]
  We propose an innovative framework, BivRec, that jointly trains the recommendation tasks in both ID and multimodal views. BivRec achieves state-of-the-art performance on five datasets and showcases various practical advantages.
  arXiv  Detail & Related papers  (2024-02-27T09:10:41Z)
• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Feature Decoupling-Recycling Network for Fast Interactive Segmentation [79.22497777645806]
  Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input. We propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies.
  arXiv  Detail & Related papers  (2023-08-07T12:26:34Z)
• Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction [13.454953507205278]
  Multi-Modal Relation Extraction aims at identifying the relation between two entities in texts that contain visual clues. We propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects. Our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.
  arXiv  Detail & Related papers  (2023-06-19T15:31:34Z)
• RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning [37.067605349559]
  We propose a novel Progressive Fusion Transformer called ProFormer. It integrates single-modality information into the multimodal representation for robust RGBT tracking. ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets.
  arXiv  Detail & Related papers  (2023-03-26T16:55:58Z)
• Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance [148.9832328803202]
  We model the infor-mation fusion within focal stack via graph networks. We build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
  arXiv  Detail & Related papers  (2021-10-02T00:54:39Z)
• Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition [7.147235324895931]
  This paper proposes a novel hierarchical graph network (HFGCN) model that learns more informative multimodal representations. Specifically, the proposed model fuses multimodality inputs using a two-stage graph construction approach and encodes the modality dependencies into the conversation representation. Experiments showed the effectiveness of our proposed model for more accurate AER, which yielded state-of-the-art results on two public datasets.
  arXiv  Detail & Related papers  (2021-09-15T08:21:01Z)

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.