GAP-Net: Calibrating User Intent via Gated Adaptive Progressive Learning for CTR Prediction

TL;DR

GAP-Net introduces a novel triple gating framework for CTR prediction, effectively addressing noise, dynamic user intent, and adaptive view aggregation through progressive, multi-level information refinement.

Contribution

The paper presents GAP-Net, a unified model with triple gating mechanisms that enhance user intent calibration and noise suppression in CTR prediction.

Findings

Significantly outperforms state-of-the-art baselines on industrial datasets.

Demonstrates robustness against interaction noise and intent drift.

Effectively calibrates user intent with dynamic, adaptive gating mechanisms.

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.