Shao-Chuan Lin

LazFormer: Scaling Transformers for Industrial Recommendation via Transferable Generative Pre-training

A transferable residual adapter that injects additional ranking-specific features into ranking in a residual manner and an asymmetric multi-epoch training strategy that resets sparse parameters while continuously accumulating dense parameters across epochs, alleviating the overfitting of sparse parameters are proposed.