Dream Sparse, Connect Dense: A Lightweight Graph Prototype Dreaming Network for Cold-Start Sequential Recommendation

Sleep replay and dreaming in the human brain reactivate sparse daytime experiences and recombine them into structured neural trajectories. This process consolidates memories and facilitates future behavior anticipation. In sequential recommendation, data sparsity and cold-start issues are equally prevalent. Yet existing methods either rely on costly auxiliary information to compensate for sparse interactions, or employ sophisticated architectures that incur heavy computational overhead. Drawing an analogy to the human dreaming mechanism, we propose a Lightweight Graph Prototype Dreaming network (LightGPD) that translates the dreaming process into a structured data augmentation framework to enhance sequential recommenders in cold-start scenarios. Specifically, we first introduce a prototype learning module that discovers abstract interest prototypes from item embeddings via subspace alignment, providing the fundamental “memory traces” for subsequent augmentation. We then devise a dreaming module that augments sparse interactions through four sequential phases: interaction replay, abstraction, hypothesis generation and verification, and dynamic graph reconstruction. This module selectively reinforces prototype-aligned interactions and discovers new connections based on learned interest patterns, enriching the sparse training signal without requiring auxiliary modalities or user attributes. Extensive experiments on three real-world datasets demonstrate that LightGPD outperforms nine state-of-the-art baselines in both accuracy and efficiency. The code and datasets are available at https://anonymous.4open.science/r/LightGPD