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.

翻译：用户在线上留下的行为足迹使企业能够发现基于行为的用户分群（简称分群），并向用户投放特定分群的消息。在发现分群后，通过Facebook和Google等首选媒体渠道向用户投放消息可能面临挑战，因为行为分群中的用户仅有一部分能在媒体中找到匹配，而这些匹配用户中又只有部分实际看到消息（曝光）。即使发现质量极高，若投放失败也徒劳无功。现有许多成熟算法可用于发现行为分群，但这些算法均忽略了投放环节。问题因以下两点而复杂化：（i）发现过程基于企业数据中的行为数据空间（如用户点击），而投放过程则依赖媒体定义的静态数据空间（如地理位置、年龄）；（ii）企业需在预算约束下运作。我们提出了一种基于随机优化的算法，用于实现投放优化的行为用户分群发现，并提供了新指标以解决联合优化问题。我们结合预算约束下的投放优化与基于学习的发现组件，在Google公开数据集和某专有数据集上进行的大量实验表明，该方法能同时提升投放指标、降低预算消耗，并实现强大的发现预测性能。