Hypercomplex Prompt-aware Multimodal Recommendation

• URL: http://arxiv.org/abs/2508.10753v1
• Date: Thu, 14 Aug 2025 15:36:00 GMT
• Title: Hypercomplex Prompt-aware Multimodal Recommendation
• Authors: Zheyu Chen, Jinfeng Xu, Hewei Wang, Shuo Yang, Zitong Wan, Haibo Hu,
• Abstract summary: We propose HPMRec, a novel Hypercomplex Prompt-aware Multimodal Recommendation framework.<n>We show that HPMRec achieves state-of-the-art recommendation performance in experiments on four public datasets.
• Score: 6.862998546677475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern recommender systems face critical challenges in handling information overload while addressing the inherent limitations of multimodal representation learning. Existing methods suffer from three fundamental limitations: (1) restricted ability to represent rich multimodal features through a single representation, (2) existing linear modality fusion strategies ignore the deep nonlinear correlations between modalities, and (3) static optimization methods failing to dynamically mitigate the over-smoothing problem in graph convolutional network (GCN). To overcome these limitations, we propose HPMRec, a novel Hypercomplex Prompt-aware Multimodal Recommendation framework, which utilizes hypercomplex embeddings in the form of multi-components to enhance the representation diversity of multimodal features. HPMRec adopts the hypercomplex multiplication to naturally establish nonlinear cross-modality interactions to bridge semantic gaps, which is beneficial to explore the cross-modality features. HPMRec also introduces the prompt-aware compensation mechanism to aid the misalignment between components and modality-specific features loss, and this mechanism fundamentally alleviates the over-smoothing problem. It further designs self-supervised learning tasks that enhance representation diversity and align different modalities. Extensive experiments on four public datasets show that HPMRec achieves state-of-the-art recommendation performance.

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