# Hypercomplex Prompt-aware Multimodal Recommendation

arXiv: https://arxiv.org/abs/2508.10753 · HTML: https://mlsanity.com/paper/2508.10753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.04320/) (2026)
- [F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting](https://mlsanity.com/paper/2608.09082/) (2026)

## Abstract (arXiv)

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.