Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation Article Swipe
Junhui Hu
,
Bryan Hooi
,
Bingsheng He
,
Yinwei Wei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i11.33283
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i11.33283
Multimodal recommendation systems can learn users' preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing methods model this through a multimodal user-item graph, approaching multimodal recommendation as a graph learning task. Graph Neural Networks (GNNs) have shown promising performance in this domain. Prior research has capitalized on GNNs' capability to capture neighborhood information within certain receptive fields (typically denoted by the number of hops, K) to enrich user and item semantics. We observe that the optimal receptive fields for GNNs can vary across different modalities. In this paper, we propose GNNs with Modality-Independent Receptive Fields, which employ separate GNNs with independent receptive fields for different modalities to enhance performance. Our results indicate that the optimal K for certain modalities on specific datasets can be as low as 1 or 2, which may restrict the GNNs' capacity to capture global information. To address this, we introduce a Sampling-based Global Transformer, which utilizes uniform global sampling to effectively integrate global information for GNNs. We conduct comprehensive experiments that demonstrate the superiority of our approach over existing methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v39i11.33283
- https://ojs.aaai.org/index.php/AAAI/article/download/33283/35438
- OA Status
- diamond
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409364994
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409364994Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v39i11.33283Digital Object Identifier
- Title
-
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal RecommendationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-11Full publication date if available
- Authors
-
Junhui Hu, Bryan Hooi, Bingsheng He, Yinwei WeiList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v39i11.33283Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/33283/35438Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/33283/35438Direct OA link when available
- Concepts
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Modality (human–computer interaction), Computer science, Artificial neural network, Transformer, Artificial intelligence, Graph, Engineering, Theoretical computer science, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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