Zhaojie Liu

Behavior-derived rewards enable MLLMs to generate effective recommendations without user-specific data at inference, outperforming traditional methods.

RGD reshapes the decoding process in generative recommendation, ensuring high-value candidates are prioritized without retraining the model.

A unified model that seamlessly integrates generative recall and multi-objective ranking achieves significant performance improvements in real-world recommendation systems.

AgentX can autonomously iterate on recommendation algorithms, outpacing human-driven processes and fundamentally changing how we approach system development.

A novel approach that boosts LTV prediction for billions of low-activity users by transforming sparse profiles into actionable insights without heavy LLM reliance.

Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.

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