# Constrained user-item allocation for e-commerce marketing campaigns

arXiv: https://arxiv.org/abs/2606.09623 · HTML: https://mlsanity.com/paper/2606.09623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Jointly Learning to Recommend and Advertise](https://mlsanity.com/paper/2003.00097/) (2020)
- [Non-stationary Bandits with Knapsacks](https://mlsanity.com/paper/2205.12427/) (2022)
- [Local algorithms for interactive clustering](https://mlsanity.com/paper/1312.6724/) (2013)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [Non-Stationary Bandits with Auto-Regressive Temporal Dependency](https://mlsanity.com/paper/2210.16386/) (2022)
- [Budgeted Embedding Table For Recommender Systems](https://mlsanity.com/paper/2310.14884/) (2023)
- [General Tensor Spectral Co-clustering for Higher-Order Data](https://mlsanity.com/paper/1603.00395/) (2016)
- [On the Consistency of Average Embeddings for Item Recommendation](https://mlsanity.com/paper/2308.12767/) (2023)
- [Spread, then Target, and Advertise in Waves: Optimal Budget Allocation Across Advertising Channels](https://mlsanity.com/paper/1702.03432/) (2017)
- [UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing](https://mlsanity.com/paper/2307.09989/) (2023)

## Abstract (arXiv)

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.