Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content. To support this journey, we must distribute commerce content when it helps, not when it distracts. We frame this as a causal decision of triggering shopping candidate generators in early retrieval and deploy a production system at Pinterest that learns personalized and contextualized triggering policies. A deep multi-task model jointly predicts outcomes and uplift of multiple events, trained with a doubly-robust pseudo-outcome alongside calibrated outcome losses for stable, single-robust uplift learning. A randomized data logging supplie...
| Authors | Junpeng Hou, XianXing Zhang, Sai Xiao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 July 2026 |
| Last updated at source | 21 July 2026 |
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