RFM: Refined Feature Modeling for Multimodal Recommendation

This paper addresses the challenge of multimodal recommendation in consumer-facing e-commerce platforms, where users interact with item information across multiple modalities—such as images, textual descriptions, and specifications. Effectively modeling these items and user preferences requires robust techniques for extracting meaningful multimodal features. To address this, we propose RFM (Refined Feature for E-commerce Multimodal Recommendation), a framework that improves user and item modeling through attention-based cross-layer knowledge distillation. RFM enables a student network to selectively learn from semantically aligned teacher layers, capturing features most relevant to user preferences while avoiding noisy or mismatched signals. These refined features are then used as item embeddings in a dual-graph learning structure, which includes both an item-item similarity graph and a user-item interaction graph. This design enhances the expressive power of item representations while preserving collaborative signals. RFM is particularly applicable to consumer-facing e-commerce platforms like Amazon, where users interact with multimodal content to evaluate a wide range of consumer electronics products. Extensive experiments on five real-world Amazon datasets demonstrate that RFM consistently outperforms strong baselines, achieving an average improvement of 5.8% in Recall@20. These results highlight the practical effectiveness of RFM for enhancing recommendation quality in online retail environments.