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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.
A Transformer-based ranking model can boost e-commerce orders by 6.35% while halving latency, thanks to optimizations targeting feature sparsity and low label density.
