Baseline and Incremental: User, Item, and Request Debiasing in Instagram Feed Ranking
Read the original on arXiv Machine Learning →The paper proposes a multi‑signal value model for Instagram Feed ranking that separates baseline predictions from incremental gains, arguing that retaining raw predictions and adding incremental terms improves ranking quality. It describes how three independent debiasing families—viewer habit, item popularity, and request context—are combined into a single parameterized operator, with a design procedure selecting optimal points. Controlled ablation studies show that the two‑term model outperforms single‑term variants in most metrics, and the approach has been adopted across Meta’s products, boosting key engagement metrics.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.