预算约束下行为用户细分中的交付优化发现
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
AI总结：
针对预算约束下行为用户细分中交付失败问题，提出基于随机优化的交付优化发现算法，联合优化交付与发现，实验证明提升交付指标、降低预算并保持强预测性能。
摘要：
用户在线的行为足迹使企业能够发现基于行为的用户细分（即细分），并向用户传递细分特定的消息。在发现细分之后，通过如Facebook和Google等首选媒体渠道向用户传递消息可能具有挑战性，因为行为细分中只有一部分用户能在媒体中找到匹配，而匹配用户中只有一部分实际看到消息（曝光）。当交付失败时，即使高质量的发现也变得徒劳。存在许多用于发现行为细分的复杂算法；然而，这些算法忽略了交付组件。问题因以下原因而加剧：（i）发现是在企业数据中的行为数据空间（例如，用户点击）上进行的，而交付则基于媒体定义的静态数据空间（例如，地理位置、年龄）；以及（ii）企业在预算约束下运作。我们引入了一种基于随机优化的算法，用于行为用户细分的交付优化发现，并提供新的指标来解决联合优化问题。我们利用预算约束下的优化来处理交付，并结合基于学习的组件进行发现。在来自Google的公共数据集和专有数据集上进行的大量实验表明，我们的方法通过同时改善交付指标、减少预算支出并在发现中实现强预测性能，展示了其有效性。
英文摘要：
Users' behavioral footprints online enable firms to discover behavior-based user segments (or, segments) and deliver segment specific messages to users. Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure). Even high quality discovery becomes futile when delivery fails. Many sophisticated algorithms exist for discovering behavioral segments; however, these ignore the delivery component. The problem is compounded because (i) the discovery is performed on the behavior data space in firms' data (e.g., user clicks), while the delivery is predicated on the static data space (e.g., geo, age) as defined by media; and (ii) firms work under budget constraint. We introduce a stochastic optimization based algorithm for delivery optimized discovery of behavioral user segmentation and offer new metrics to address the joint optimization. We leverage optimization under a budget constraint for delivery combined with a learning-based component for discovery. Extensive experiments on a public dataset from Google and a proprietary dataset show the effectiveness of our approach by simultaneously improving delivery metrics, reducing budget spend and achieving strong predictive performance in discovery.
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