Li Wei

TokenMinds reveals that combining discrete SID-based user tokens with dense embeddings can significantly enhance user modeling in recommender systems at scale.

Transforming traditional signals into "soft tokens" could revolutionize how we integrate diverse data into Large Recommendation Models without compromising performance.

Density-dependent shifts in the potential of zero charge reveal a more accurate predictor for oxygen reduction activity than traditional magnetic descriptors in M-N-C electrocatalysts.

You can boost ranking model performance in low-traffic recommendation systems by directly distilling knowledge from a large-scale, but different, domain like video recommendations.