Multimodal recommender systems (MMRec) leverage multimodal features, such as visual and textual data, to improve recommendation performance, playing a key role in platforms like online shopping and short videos. However, the large modality encoders and complex processing modules of MMRec significantly reduce its efficiency. A promising solution is compressing MMRec into an ID-based MLP model (MLPRec), which has a simpler structure and avoids complex modality handling. However, traditional knowledge distillation methods struggle to transfer knowledge effectively from MMRec to MLPRec, due to differences in their model structure and capacity. To address this, we propose a frequency-decoupled knowledge distillation framework-FDRec-to efficiently transfer knowledge from MMRec to MLPRec. By analyzing graph signals from a signal processing perspective, we propose decoupling the distillation process into low-frequency and high-frequency components, ensuring effective transmission of challenging high-frequency knowledge while preventing it from being overshadowed by monotonous low-frequency signals. To address the instability and fragmentation issues of KL divergence in traditional distillation approaches, we introduce the Wasserstein distance, which captures geometric structure and provides stable gradients. Additionally, FDRec incorporates an embedding-level contrastive learning method, further enhancing the transfer of refined knowledge from MMRec and injecting graph structure information into MLPRec for more effective distillation. Extensive experiments on four benchmark datasets and five popular MMRec models show that FDRec not only significantly reduces the computational costs and improves the inference efficiency, but also achieves comparable or even superior performance compared to MMRec. Our code is available at: https://github.com/Suehn/FDRec_

The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities-like text, images, videos, and audio-for more personalized recommendations. However, MMRecs struggle with the challenge of noisy data from the misalignment among modal content and the difference between modal and recommendation semantics, while traditional denoising methods fall short in addressing these issues due to the complexity of multi-modal data. To overcome this, we propose a universal guided in-sync distillation denoising framework for multi-modal recommndation (GUIDER), aimed at improving MMRecs by denoising user feedbacks. Specifically, GUIDER employs a re-calibration strategy to identify clean and noisy interactions from modal content. Furthermore, it incorporates a Denoising Bayesian Personalized Ranking (DBPR) loss function to denoise implicit user feedback. Finally, it utilizes a denoising knowledge distillation objective based on Optimal Transport (OT) distance to guide the mapping from modality representations to recommendation semantics spaces. GUIDER can be seamlessly integrated into existing MMRecs methods as a plug-and-play solution for recommendation denoising. Experiment results on four public datasets show its effectiveness and universality across various MMRecs.

Multi-modal recommender systems (MMRecs) leverage diverse modalities to deliver personalized recommendations, yet they often struggle with efficiency due to the large size of modality encoders and the complexity of fusing high-dimensional features. To address the efficiency issue, a promising solution is to compress a cumbersome MMRec into a lightweight ID-based Multi- Layer Perceptron-based Recommender system (MLPRec) through Knowledge Distillation (KD). Despite effectiveness, we argue that this approach overlooks the significant gap between the complex teacher MMRec and the lightweight, ID-based student MLPRec, which differ significantly in size, architecture, and input modalities, leading to ineffective knowledge transfer and suboptimal student performance. To bridge this gap, we propose TARec, a novel teacher-assisted Wasserstein Knowledge Distillation framework for compressing MMRecs into an efficient MLPRec. TARec introduces: (i) a two-staged KD process using an intermediate Teacher Assistant (TA) model to bridge the gap between teacher and student, facilitating smoother knowledge transfer; (ii) logit-level KD using the Wasserstein Distance as metric, replacing the conventional KL divergence to ensure stable gradient flow even with significant teacher-student gaps; and (iii) embedding-level contrastive KD to further distill high-quality embedding-level knowledge from teacher. Extensive experiments on real-world datasets verify the effectiveness of TARec, demonstrating that TARec significantly outperforms the state-of-the-art MMRecs while reducing computational costs. Our code is available at: https://github.com/Suehn/TARec.git.