用深度因果学习优化电商推荐触发，减少干扰同时提升用户参与
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
- 构建多任务模型联合预测行为与增益，结合双重稳健伪结果稳定训练
- 线上减少85%购物触发，总会话量+0.26%，保存数+1.10%，基础设施成本显著下降
- 支持生产级部署，适配大规模推荐系统中的早期筛选优化
Pinterest 上用户将灵感转化为行动，常通过发现可购买内容实现。为支持这一过程，需在合适时机分发电商内容，避免干扰。本文将此问题建模为早期检索中触发购物候选生成器的因果决策，并部署了个性化与情境化触发策略的生产系统。采用深度多任务模型联合预测多种事件的结果与增益，使用双重稳健伪结果配合校准结果损失，实现稳定、单重稳健的增益学习。通过随机数据日志获取反事实覆盖，模型以常规与逆向指标综合评估。设计线性时间离线回放机制，高一致性地选择阈值并预测策略影响。系统与远程检索并行运行，无端到端延迟增加。在网页规模下，购物触发最多减少85%，关键购物会话保持不变，总会话量提升0.26%，点赞数提升1.10%，显著降低基础设施开销。本工作融合深度因果学习与可靠离线回放，为现代级联推荐系统中早期检索优化提供通用可行方案，实现探索与成本对齐用户意图。
原文摘要 · Abstract (English)
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.
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