GSRT: Granularity-Selected Residual Transfer for Sparse Targets in Two-Tower Retrieval

Rare target events invite transfer from abundant auxiliary feedback, but the granularity of that transfer is an open choice: one global coefficient ignores heterogeneity, while per-user coefficients overfit sparse labels. We study this choice for transferring consumption feedback to sparse creator actions in two-tower retrieval. Under a local risk approximation we characterize the supportable granularity by an information budget, the heterogeneity of the transfer ormation the target labelscarry about the transfer coefficient: above a criterion, group-level coefficients have lower predicted risk than a global one, and a detection lower bound shows that far below it no procedure can exploit gris analysis we proposeGranularity-Selected Residual Transfer (GSRT), which adds a source-derived residual to a frozen target-only predictor, selects among no transfer and global, group and instance-level coefficients byand serves transfer only whenan evidence gate rejects no transfer. Because the coefficient is user-side, the score keeps an exact inner-product factorization, so one approximate-nearest-neighbour index petarget serves it. On a 4,800-cempirical boundary fallsbetween the two thresholds and the gate cuts cells with negative transfer from 30% to 5%. On production logs of a short-video platform, where the target model sees fewer than 100 positives, selecting the coeffating probabilities separatelyraises recall inside a 1% exposure quota by 0.062 without increasing Brier loss, and the gain vanishes as the target model is given more labels. On public logs where no transferable signal is detecteith no significant loss whileforced transfer loses.