GAP-Net：通过门控自适应渐进学习校准用户意图以进行CTR预测
GAP-Net: Calibrating User Intent via Gated Adaptive Progressive Learning for CTR Prediction
AI总结：
GAP-Net通过门控自适应渐进学习校准用户意图，提升CTR预测的鲁棒性与准确性。
摘要：
顺序用户行为建模对点击通过率（CTR）预测至关重要，但受到三个内在瓶颈的阻碍：（1）'注意力沉没'现象，其中标准Softmax迫使模型将概率质量分配给噪声行为；（2）静态查询假设，忽略了由实时上下文驱动的用户意图动态变化；（3）刚性视图聚合，无法根据决策上下文自适应加权异质时间信号。为弥合这些差距，我们提出了GAP-Net（门控自适应渐进网络），一个统一的框架，建立了一个'三门控'架构，逐步从微级特征到宏观视图细化信息。GAP-Net通过三个集成机制运作：（1）自适应稀疏门控注意力（ASGA）利用微级门控强制稀疏性，有效抑制大量噪声激活；（2）门控级联查询校准（GCQC）通过中等级级联通道动态对齐用户意图，通过实时触发和长期记忆之间的桥梁；（3）上下文门控去噪融合（CGDF）在宏观层面调节，以协调多视图序列的聚合。在工业数据集上的广泛实验表明，GAP-Net在现有最先进基线上实现了显著改进，表现出对交互噪声和意图漂移的更强鲁棒性。
英文摘要：
Sequential user behavior modeling is pivotal for Click-Through Rate (CTR) prediction yet is hindered by three intrinsic bottlenecks: (1) the "Attention Sink" phenomenon, where standard Softmax compels the model to allocate probability mass to noisy behaviors; (2) the Static Query Assumption, which overlooks dynamic shifts in user intent driven by real-time contexts; and (3) Rigid View Aggregation, which fails to adaptively weight heterogeneous temporal signals according to the decision context. To bridge these gaps, we propose GAP-Net (Gated Adaptive Progressive Network), a unified framework establishing a "Triple Gating" architecture to progressively refine information from micro-level features to macro-level views. GAP-Net operates through three integrated mechanisms: (1) Adaptive Sparse-Gated Attention (ASGA) employs micro-level gating to enforce sparsity, effectively suppressing massive noise activations; (2) Gated Cascading Query Calibration (GCQC) dynamically aligns user intent by bridging real-time triggers and long-term memories via a meso-level cascading channel; and (3) Context-Gated Denoising Fusion (CGDF) performs macro-level modulation to orchestrate the aggregation of multi-view sequences. Extensive experiments on industrial datasets demonstrate that GAP-Net achieves substantial improvements over state-of-the-art baselines, exhibiting superior robustness against interaction noise and intent drift.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
