SlimPer: A Slim and Smart Personalization Model for Recommendation Systems

Jul 15, 2026

SlimPer introduces a new approach to personalized ranking in recommendation systems by iteratively refining a compact knowledge base, rather than relying on large intermediate representations that scale with user history length. This design achieves O(N) per-layer computational cost and fixed-size intermediate representations, allowing the model to handle over 10,000 user history events efficiently. Deployed on Instagram Reels and Feed, SlimPer has demonstrated measurable improvements in user engagement while unifying sparse, dense, and sequence features within a single model backbone.

Why it matters: SlimPer enables deeper and more efficient personalization in large-scale recommendation systems by decoupling model depth from user history length, reducing compute and memory requirements.

Full story at: arXiv Information Retrieval ↗