Baseline and Incremental: User, Item, and Request Debiasing in Instagram Feed Ranking
A feed ranker scores candidates with a value model, a weighted sum of engagement predictions. Each prediction conflates a baseline, what this viewer, item or request would produce anyway, with the incremental amount by which this candidate exceeds it. The literature mostly removes the baseline. We argue that a multi-signal value model should keep the raw prediction and add the incremental part as a separately weighted term. On Instagram Feed, three debiasing families deployed independently, for viewer habit, item popularity and request context, are one parameterised operator, and a design procedure picks a point in it. Media-type (format) bias goes instead to a debiased label chosen by th...
| Authors | Hsin-Chiang Chen, Hao Wang, Gedi Zhou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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