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RGD reshapes the decoding process in generative recommendation, ensuring high-value candidates are prioritized without retraining the model.
Behavior-derived rewards enable MLLMs to generate effective recommendations without user-specific data at inference, outperforming traditional methods.
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.
Retention models can now harness the power of post-conversion content without risking feature leakage, leading to more accurate predictions of user engagement.
Diffusion models can now generate user preferences for multi-behavior sequential recommendation, outperforming traditional methods by better capturing uncertainty and enabling more diverse recommendations.
