Equip Pre-ranking with Target Attention by Residual Quantization 事件

Equip Pre-ranking with Target Attention by Residual Quantization arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This d