CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems

TL;DR

CADMR introduces a novel autoencoder framework utilizing cross-attention and disentangled learning to effectively integrate multimodal data, significantly improving recommendation accuracy in sparse, high-dimensional settings.

Contribution

It proposes a new autoencoder-based multimodal recommender system with multi-head cross-attention and disentangled learning, enhancing data integration and matrix reconstruction.

Findings

Significant performance improvements over state-of-the-art methods.

Effective disentanglement of modality-specific features.

Enhanced user-item interaction representations.

Abstract

The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item rating matrices, where reconstructing the matrix with only small subsets of preferred items for each user poses a significant challenge. To address this, we propose CADMR, a novel autoencoder-based multimodal recommender system framework. CADMR leverages multi-head cross-attention mechanisms and Disentangled Learning to effectively integrate and utilize heterogeneous multimodal data in reconstructing the rating matrix. Our approach first disentangles modality-specific features while preserving their interdependence, thereby learning a joint latent representation. The multi-head cross-attention mechanism is then applied to enhance user-item interaction representations with respect to the learned multimodal item latent representations. We evaluate CADMR on three benchmark datasets, demonstrating significant performance improvements over state-of-the-art methods.