电商营销团队终于有了一个能自动发现用户-商品匹配分组的框架，比传统解耦方法更贴合实际业务。做推荐系统或营销策略的工程师可以直接参考其中的双聚类方法，提升活动效果。
本文提出了一种新的电商营销问题形式化——自动定向（auto-targeting），即联合选择用户和商品以构建多个互不重叠的营销活动。现有方法通常预设活动结构或解耦商品选择与用户分配，无法直接从交互模式中发现活动分组。作者提出了三种互补策略：约束谱双聚类、贪心局部搜索与多臂老虎机框架。在合成数据、Amazon评论基准和商业数据上的实验表明，双聚类方法在活动质量、提升度和公平性上表现最佳，但大规模数据下基于老虎机的方法更具扩展性。
Constrained user-item allocation for e-commerce marketing campaigns
When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: effective campaigns match users and items with strong mutual affinity into non-overlapping groups of predefined sizes. However, existing approaches assume predefined campaign structure or decouple item selection from user assignment, and cannot discover campaign groupings directly from joint interaction patterns. We therefore formalize this campaign problem as auto-targeting: jointly selecting users and items to construct multiple disjoint campaigns. To solve this combinatorial problem, we propose three complementary strategies: (i) constrained spectral biclustering to find dense regions in the user-item affinity matrix, (ii) greedy local search with pairwise swaps for combinatorial refinement, and (iii) a multi-armed bandit framework to escape local optima through exploration. We evaluate these methods on a synthetic dataset, the Amazon Reviews benchmarks, and large-scale proprietary commercial data, and compare the results to simulated annealing as a baseline. The results show that biclustering consistently achieves the highest campaign quality, lift, and fairness scores. While biclustering runs efficiently on smaller datasets, its runtime increases substantially on very large ones, where bandit-based methods instead offer a scalable alternative.