在 Uber 通过 Sequential Modeling 和 Hetero-MMoE 变革广告个性化

Personalization lies at the heart of an effective ads delivery system. On Uber’s platform, this means ensuring every sponsored placement is relevant and timely, context-aware, and aligned with each person’s evolving preferences. Our ads delivery system leverages large-scale machine learning models that continuously learn from behavioral signals, such as past orders, user engagements, and geographic context, to predict the most relevant ads for every impression opportunity. By optimizing across multiple objectives like user engagement, advertiser performance, and marketplace health, the system delivers more meaningful recommendations to people while helping advertisers reach audiences with higher intent. This balance of personalization and efficiency, powered by scalable multi-task ML infrastructure and real-time decisioning, enables a dynamic ecosystem where ads enhance discovery rather than disrupt it.

个性化是有效广告投放系统的核心。在 Uber 平台上，这意味着确保每个赞助位置都相关、及时、上下文感知，并与每个人的演变偏好一致。我们的广告投放系统利用大规模机器学习模型，不断从行为信号（如过去订单、用户互动和地理上下文）中学习，以预测每个展示机会的最相关广告。通过在用户互动、广告主性能和市场健康等多个目标上优化，系统为用户提供更有意义的推荐，同时帮助广告主接触到更高意图的受众。这种个性化与效率的平衡，由可扩展的多任务 ML 基础设施和实时决策驱动，实现了广告增强发现而非干扰的动态生态系统。

Figure 1: Overview of the Ads ranking stack.

图 1：Ads ranking stack 概述。

As our ads delivery system matured, two key limitations surfaced in the existing architecture. First, our reliance on largely static, aggregate features flattened rich temporal user behaviors into summary statistics, such as total clicks or impressions within a fixed time window. While effective for short-term modeling, this approach lost crucial ordering, recency, and long-term context, capturing only snapshots of user behavior rather than evolving intent. To better represent the dynamics of user engagement, we introduced sequential user features powered by a target-aware transformer encoder, enabling the model to preserve fine-grained interaction patterns and reason over a user’s lifelong engagement history.

随着我们的广告投放系统成熟，现存架构中出现了两个关键限制。首先，我们对主要静态、聚合特征的依赖，将丰富的时序用户行为扁平化为摘要统计，如固定时间窗口内的总点击或展示。虽然对短期建...