CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems
This work addresses the problem of enhancing recommendation accuracy and user satisfaction for users in multimodal recommender systems, representing an incremental advancement through a novel hybrid method.
The paper tackled the challenge of high-dimensional, sparse user-item rating matrices in multimodal recommender systems by proposing CADMR, an autoencoder-based framework that integrates heterogeneous multimodal data using cross-attention and disentangled learning, achieving significant performance improvements over state-of-the-art methods on three benchmark datasets.
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.