Instagram Feed — Keeping the Baseline in Ranking

Instagram Feed's ranker stops deleting "bias": Meta keeps the raw engagement prediction and adds its debiased, incremental part as a second, separately weighted term. Key takeaways: Baseline vs incremental. Each value-model prediction mixes a baseline (a serial liker, a popular creator, a strong batch) with the candidate's own excess. Most prior work removes the baseline; Meta keeps the raw term and weights the incremental term separately (Eq. 1–2). No causal claim.One operator, three axes. Viewer, item and request debiasing are one operator (Eq. 3; per-term forms in Table 4). The request baseline is the p75 or mean of predictions over the request's own candidates, not time of day or device.Six four-arm live ablations (none/raw/incremental/both, 3% of traffic per arm, one week). Both beats each single term on point estimates in 11 of 13 pre-assigned (term, metric) pairs, 7 with both contrasts significant; 17 of 26 contrasts significantly for, 1 against (impressions).Beyond Feed. Separate Reels launches (Table 6): sessions +0.029% to +0.083%, DAU +0.026% to +0.042%.Our caveats. A Meta-authored case series of six terms; production weights unpublished; single-term arms not re-tuned; small session effects. Offline diagnostics should screen signals, not rank them.Chapters: 00:00 Cold open: why your feed shows what it does 00:58 The paper: Baseline and Incremental 01:56 The value model's two hidden parts 04:10 Removing the baseline vs keeping it 06:30 Two terms and one operator (Eq. 1–3) 07:58 Request context = the live candidate pool 10:26 The four-arm live ablation 11:58 Reels launches: sessions and DAU 13:15 Judged pairs and multiplicity 15:00 Results: 11 of 13, 17 of 26, one loss 16:11 Novelty and our caveats 17:54 Offline diagnostics: screen, don't rank 18:53 Related work: testing in the wild 20:16 Recap and closing question Sources: Baseline and Incremental: User, Item, and Request Debiasing in Instagram Feed Ranking (Chen, Wang, Zhou et al., Meta; arXiv, Oct 20, 2026)Selling in the Wild: Benchmark and Randomized Field Evidence for a Sales Agent Trained on Commercial Conversations (Tannenbaum; arXiv, 2026; our S2E9)Don't Rewrite the SID Map: Topology Preservation for Generative Retrieval in Dynamic E-Commerce Search (Zhu et al., Alibaba; arXiv, 2026)Enjoyed it? Follow Data Science in the Wild (one real production ML/AI system or paper per episode, hosted by Peter Liu) and rate it on Apple Podcasts or Spotify. Also from Peter: The GenAI Evolution Atlas, Wednesday AI deep dives and Friday news roundups. Made with AI-generated voices; curated by Peter Liu from the sources above.