MAPLE: Mamba-Based Adaptive Prototype Learning for Intent Disentanglement in Sequential Recommendation
Sequential recommender systems struggle to simultaneously capture persistent long-term preferences and volatile short-term intentions, while existing dual-stream architectures are computationally inefficient when handling long sequences and suffer from feature entanglement between the two interest streams. To address t...