Kun Gai

Retention models can now harness the power of post-conversion content without risking feature leakage, leading to more accurate predictions of user engagement.

Reranking in recommender systems can be revolutionized by shifting from local indices to generating global identifiers, enhancing robustness and user satisfaction.

UniMixer achieves state-of-the-art scaling in recommendation systems by unifying disparate architectures into a single framework that learns optimal token mixing patterns.

SVD-Attention slashes the quadratic cost of attention to linear for recommendation tasks by exploiting the inherent low-rank structure of user behavior sequences, without sacrificing softmax.

FlashEvaluator slashes the computational cost of evaluating multiple sequences in Generator-Evaluator frameworks while boosting accuracy by enabling direct cross-sequence comparisons.

Achieve lossless acceleration of ranking models by structurally re-parameterizing feature fusion matrix multiplication, sidestepping the accuracy drop common in lightweighting and distillation.