KDD 2026-MGTA: Multi-scale Graph Tokens Alignment for CTR Prediction via Pre-trained Language Models @TheOfficialACM
KDD 2026-MGTA: Multi-scale Graph Tokens Alignment for CTR Prediction via Pre-trained Language Models @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Zhongzhen Wu, Yating Ren, Shuochen Li, Huobin Tan
KDD 2026-MGTA: Multi-scale Graph Tokens Alignment for CTR Prediction via Pre-trained Language ModelsKDD2026-BridgingFront-Door Adjustment and Information Bottleneck for Identifiable Causal Represent.Research to Reality Building Production Ready LLM Apps Users Can TrustByteCast Ep85: Eric AllmanSeptember 2026 CACM: Cash or Comfort: How LLMs Value Your InconvenienceCAIS 2026 | Securing Gemini from Cyberattack MisuseKDD2026-BrokenMemories:Detecting andMitigatingMemorization in Diffusion Models with Degraded Gener.MARVIS: Modality Adaptive Reasoning over VISualizationsKDD 2026 - Bounded-Abstention Pairwise Learning to RankKDD 2026 - PinEqualizer: Full Funnel Content Exploration and Debiasing System at PinterestKDD 2026 - ExDBSCAN: Explaining DBSCAN with Counterfactual ReasoningKDD 2026 - RUQuant: Towards Refining Uniform Quantization for Large Language Models
Association for Computing Machinery (ACM) |

KDD 2026-MGTA: Multi-scale Graph Tokens Alignment for CTR Prediction via Pre-trained Language Models

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