Ge Song

SlimPer achieves deeper relevance understanding in recommendation systems without the computational bloat, leading to measurable boosts in user engagement on platforms like Instagram.

Storing user interaction histories in a normalized, immutable tier and reconstructing sequences just-in-time slashes data infrastructure costs and unlocks the potential of ultra-long sequence DLRMs.