Google scales learned cross-task relationships in YouTube's production recommender
_reachsumit · x · 2026-09-25
A RecSys 2026 paper from Google proposes a framework for learning cross-task relationships in multi-task models: approximating the joint distribution of task labels through targeted pairwise relationships, gaining transfer-learning benefits without the intractable complexity of modeling the full joint space.
- The approach uses auxiliary heads to estimate cross-task covariance within multi-task models.
- It is deployed in YouTube's production recommendation systems, with experiments across the Notifications, Homepage, and Watch Next surfaces showing improvements in both accuracy and user satisfaction metrics.
- The authors also propose a workflow template to facilitate broader adoption in other multi-task systems.
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