DeCoRec: Decoupled Collaborative Refinement for Multi-Modal Sequential Recommendations

摘要

While multi-modal features offer rich semantic signals to enhance sequential recommendation systems, their integration with ID-based embeddings remains challenging. Conventional fusion strategies often degrade performance despite the semantic potential of multimodal data. Through empirical analysis, we identify asymmetric convergence dynamics between rapidly adapting ID embeddings and slowly evolving modality representations as the fundamental barrier. To address this, we propose DeCoRec, a novel framework to decouple ID and modality optimization trajectories to prevent gradient interference. To further reconcile ID and multi-modal data, we introduce modality-aware interest clustering and cross-modal contrastive learning to align semantic neighborhoods with behavioral patterns. Extensive experiments demonstrate 5-7% improvements in NDCG/HiT metrics against the existing schemes and particular robustness in cold-start scenarios. The code is available: https://github.com/KIKIENAO/decorec