用于Pinterest中高效电子商务分发的深度学习因果检索优化
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
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中文总结 AI 辅助
研究在Pinterest中如何高效分发电子商务内容，通过深度多任务模型学习个性化和情境化触发策略，结合双稳健伪结果训练及随机数据记录等方法，实现早期检索优化，大幅减少购物触发，提升会话数和保存数，节省成本。
中文摘要 AI 辅助
Pinterest是人们将灵感转化为行动的平台，用户浏览想法并采取实现步骤，常通过发现可购物内容来实现。为支持这一过程，需在有助于而非干扰时分发商业内容。我们将此视为早期检索中触发购物候选生成器的因果决策，并在Pinterest部署了一个生产系统，该系统学习个性化和情境化触发策略。一个深度多任务模型联合预测多个事件的结果和提升，使用双稳健伪结果和校准结果损失进行训练以实现稳定的单稳健提升学习。随机数据记录提供反事实覆盖，通过常规和反向指标进行全面评估。设计了线性时间离线重放以选择阈值并预测策略影响，且与在线结果高度一致。在生产中，模型与远程检索调用并行运行而无端到端延迟回归。在网络规模上，我们在保持关键购物会话不变的情况下将购物触发减少多达85%，同时提升了重要的总会话数（+0.26%）和Pin保存数（+1.10%），并节省了大量基础设施成本。通过将深度因果学习与可靠的离线重放相结合并展示生产级部署，这项工作为现代级联推荐器中的早期检索优化提供了一个普遍实用的方法，在大规模上使探索和成本与用户意图保持一致。
英文摘要
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 supplies counterfactual coverage, and the model is evaluated by both regular and reverse metrics for full assessment. A linear-time offline replay is designed to select thresholds and forecast policy impact with extremely high consistency with online results. For productionization, the model runs in parallel with remote retrieval calls without end-to-end latency regression. At web scale, we cut shopping triggers by up to 85% while holding key shopping sessions neutral, improving important total sessions (+0.26%) and Pin saves (+1.10%), with significant infrastructure savings. By unifying deep causal learning with reliable offline replay and demonstrating production-grade deployment, this work provides a generally practical recipe for early-retrieval optimizations in modern cascading recommenders beyond shopping, aligning exploration and cost with user intent at scale.
发表机构
- Pinterest, Inc.（Pinterest公司）
机构由 AI 辅助整理，请以论文原文为准。
