Wei Wei

Fine-grained interest extraction in search and recommendation can dramatically boost accuracy, revealing user preferences that traditional models overlook.

Achieving state-of-the-art accuracy in 3D multi-person motion prediction hinges on a novel framework that balances structural stability with dynamic inter-agent interactions.

Unlearning in offline RL is more complex than previously thought, with common deletion methods showing environment-dependent privacy-utility behavior that can mislead evaluations.

Stop disjointedly predicting ratings and explanations in recommendation systems: Curr-RLCER uses reinforcement learning to ensure they actually make sense together.