Ching Law

Click-through rate (CTR) prediction is a pivotal component in large-scale industrial systems. Historically, CTR prediction paradigms have been confined to monolithic architectures governed by a single-policy optimization process. However, such isolated learning paths lack the intrinsic evolutiona...

Industrial generative recommendation systems operating over discrete Semantic IDs (SIDs) are largely behavior-driven, and thus struggle to proactively activate latent demand before explicit user signals emerge, leading to intent cold-start. To address this, we propose RIGER (Reasoning-grounded In...

Generative Recommendation (GenRec) has emerged as a significant evolutionary direction in the field of recommendation systems in recent years. However, during the reinforcement learning (RL) alignment stage of end-to-end GenRec, a core challenge is that the low signal-to-noise ratio (SNR) of feed...