dorsal/arxiv
View SchemaGAP-Net: Calibrating User Intent via Gated Adaptive Progressive Learning for CTR Prediction
| Authors | Shenqiang Ke, Jianxiong Wei, Qingsong Hua |
|---|---|
| Categories | |
| ArXiv ID | 2601.07613vv2 |
| URL | https://arxiv.org/abs/2601.07613 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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.
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"abstract": "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.",
"arxiv_id": "2601.07613",
"authors": [
"Shenqiang Ke",
"Jianxiong Wei",
"Qingsong Hua"
],
"categories": [
"cs.IR"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "GAP-Net: Calibrating User Intent via Gated Adaptive Progressive Learning for CTR Prediction",
"url": "https://arxiv.org/abs/2601.07613",
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