New Algorithm Framework for Optimizing E-Commerce Marketing Campaign Targeting
A new preprint introduces 'auto-targeting,' a framework that jointly selects users and products to construct multiple disjoint marketing campaigns without requiring predefined campaign structure. The approach combines constrained spectral biclustering, greedy local search, and a multi-armed bandit method, evaluated on synthetic, Amazon Reviews, and proprietary commercial datasets. The work addresses a gap in existing methods that either assume fixed campaign structures or decouple product selection from user assignment.
Researchers have formalized e-commerce campaign planning as an 'auto-targeting' problem, in which users and items are jointly selected and grouped into non-overlapping campaigns based on mutual affinity patterns in interaction data. Three complementary algorithmic strategies are proposed: constrained spectral biclustering to identify dense regions in user-item affinity matrices, greedy local search with pairwise swaps for combinatorial refinement, and a multi-armed bandit framework to escape local optima through exploration. The methods are benchmarked against simulated annealing across synthetic data, the publicly available Amazon Reviews dataset, and large-scale proprietary commercial data. Biclustering consistently achieved the highest scores on campaign quality, lift, and fairness metrics, but its runtime scaled poorly on very large datasets. In those high-volume settings, the bandit-based approach offered a more scalable alternative, suggesting a practical trade-off between solution quality and computational cost.
What's missing
The scalability threshold at which bandit methods become preferable over biclustering is not precisely quantified in the abstract.
What different sources said
- arXiv cs.LGCenter
Constrained user-item allocation for e-commerce marketing campaigns
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