A unified framework for sequential recommendation with gated differential amplified attention and repetition-exploration intent modeling.

Self-attention models in sequential recommendation face two under-explored but complementary challenges: (1) susceptibility to attention noise, especially in long-sequence modeling, and (2) difficulty in distinguishing between repetition and exploration behaviors under the Softmax bottleneck. To jointly tackle these challenges, we propose a unified framework with gated differential amplified attention and repetition-exploration intent modeling (GDA-REIM for short). Existing methods generally address these problems individually, via external denoising or architecture-level branching for repetition-exploration modeling. However, noise in attention weights can obscure true user intent (repetition vs. exploration), while clear intent boundaries can guide more effective denoising, making joint optimization essential yet unexplored in prior work. GDA-REIM incorporates a gated differential amplified attention (GDAA) module, which employs a three-stage "differentiation-gating-amplification" pipeline that computes and subtracts paired attention maps to suppress common-mode noise and dynamically rescales the denoised signal. Leveraging the resulting denoised representations, a partitioned intent scoring (PIS) component together with an intent discrimination margin (IDM) loss explicitly distinguishes repetition and exploration intent. Extensive experiments on ML-1M, Amazon-Video-Games, and Twitch-100k datasets demonstrate consistent improvements over strong baselines (e.g., approximately +10% improvement in NDCG@10, or N@10 for short, on ML-1M). Our code is released at https://anonymous.4open.science/r/GDA-REIM/.