A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals
Multimodal recommender systems leverage auxiliary item features, such as images and descriptions, to alleviate the data sparsity problem and facilitate the preference modeling process. Despite their potential, existing multimodal recommenders fail to exploit global collaborative signals and lack ins...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014073/ |
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| author | Peng Yi Lu Chen Zhaoxian Li Cheng Yang |
| author_facet | Peng Yi Lu Chen Zhaoxian Li Cheng Yang |
| author_sort | Peng Yi |
| collection | DOAJ |
| description | Multimodal recommender systems leverage auxiliary item features, such as images and descriptions, to alleviate the data sparsity problem and facilitate the preference modeling process. Despite their potential, existing multimodal recommenders fail to exploit global collaborative signals and lack insights into the underlying interaction formation mechanism, resulting in suboptimal recommendation performance. To this end, we propose a hierarchical multimodal recommender named HMMGCF, which can capture crucial global collaborative signals through modality feature-enhanced hierarchical structures and a novel inter-modality alignment strategy. Specifically, modality features are first utilized to identify neighboring relationships, and similar users (items) are steadily merged together to form modality-specific hierarchical structures. Then, with the proper graph convolution operation on each hierarchy, the crucial global collaborative signals can be effectively extracted and integrated into the modality-specific user (item) embeddings. Moreover, a novel group-wise contrastive learning strategy is also proposed to align inter-modality preference information and further enhance the extraction of global collaborative signals. By conducting extensive experiments on three benchmark datasets, we empirically and theoretically demonstrate the superiority of HMMGCF, validating the importance of global collaborative signal extraction in multimodal recommender systems. |
| format | Article |
| id | doaj-art-c8fee5e14cf64c168d1ebbf3dfee18fe |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c8fee5e14cf64c168d1ebbf3dfee18fe2025-08-20T02:19:38ZengIEEEIEEE Access2169-35362025-01-0113921029211310.1109/ACCESS.2025.357320411014073A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative SignalsPeng Yi0https://orcid.org/0009-0002-3114-6566Lu Chen1https://orcid.org/0009-0009-6253-5848Zhaoxian Li2https://orcid.org/0009-0005-7417-1739Cheng Yang3https://orcid.org/0000-0003-3873-6411State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, ChinaFaculty of Social Sciences, University of Copenhagen, Copenhagen, DenmarkState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, ChinaMultimodal recommender systems leverage auxiliary item features, such as images and descriptions, to alleviate the data sparsity problem and facilitate the preference modeling process. Despite their potential, existing multimodal recommenders fail to exploit global collaborative signals and lack insights into the underlying interaction formation mechanism, resulting in suboptimal recommendation performance. To this end, we propose a hierarchical multimodal recommender named HMMGCF, which can capture crucial global collaborative signals through modality feature-enhanced hierarchical structures and a novel inter-modality alignment strategy. Specifically, modality features are first utilized to identify neighboring relationships, and similar users (items) are steadily merged together to form modality-specific hierarchical structures. Then, with the proper graph convolution operation on each hierarchy, the crucial global collaborative signals can be effectively extracted and integrated into the modality-specific user (item) embeddings. Moreover, a novel group-wise contrastive learning strategy is also proposed to align inter-modality preference information and further enhance the extraction of global collaborative signals. By conducting extensive experiments on three benchmark datasets, we empirically and theoretically demonstrate the superiority of HMMGCF, validating the importance of global collaborative signal extraction in multimodal recommender systems.https://ieeexplore.ieee.org/document/11014073/Multimodal recommenderhierarchical graph convolutional networkscontrastive learning |
| spellingShingle | Peng Yi Lu Chen Zhaoxian Li Cheng Yang A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals IEEE Access Multimodal recommender hierarchical graph convolutional networks contrastive learning |
| title | A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals |
| title_full | A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals |
| title_fullStr | A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals |
| title_full_unstemmed | A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals |
| title_short | A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals |
| title_sort | novel hierarchical multimodal recommender with enhanced global collaborative signals |
| topic | Multimodal recommender hierarchical graph convolutional networks contrastive learning |
| url | https://ieeexplore.ieee.org/document/11014073/ |
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