Constrained user-item allocation for e-commerce marketing campaigns

2026-06-08 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new approach addresses the e-commerce marketing challenge of "auto-targeting," which involves jointly selecting users and products to construct multiple disjoint campaigns based on mutual affinity. Current methods often assume predefined campaign structures or separate user and item selection. To solve this combinatorial problem, researchers propose three strategies: constrained spectral biclustering, greedy local search with pairwise swaps, and a multi-armed bandit framework. Evaluation on a synthetic dataset, Amazon Reviews benchmarks, and large-scale proprietary commercial data, compared against simulated annealing, shows biclustering consistently delivers the highest campaign quality, lift, and fairness scores. While biclustering performs efficiently on smaller datasets, bandit-based methods provide a scalable alternative for very large datasets due to biclustering's increased runtime on such scales.

Key takeaway

For e-commerce marketing teams designing targeted campaigns, consider implementing constrained spectral biclustering to jointly optimize user-item allocation. This approach consistently yields higher campaign quality, lift, and fairness scores than traditional methods. If you manage very large datasets, explore multi-armed bandit frameworks as a scalable alternative to biclustering, which can become computationally intensive at extreme scales. Prioritize evaluating campaign performance using lift and fairness metrics.

Key insights

Constrained spectral biclustering effectively auto-targets e-commerce campaigns by jointly optimizing user-item allocation for quality and fairness.

Principles

• Joint user-item optimization improves campaign quality.
• Biclustering excels in affinity-based campaign grouping.
• Scalability varies; bandit methods suit large datasets.

Method

The proposed auto-targeting method combines constrained spectral biclustering for dense region discovery, greedy local search for refinement, and a multi-armed bandit framework for exploration to escape local optima.

In practice

• Apply biclustering for high-quality, fair campaign groups.
• Consider bandit methods for very large user-item datasets.
• Evaluate campaign lift and fairness metrics.

Topics

• E-commerce Marketing
• User-Item Allocation
• Spectral Biclustering
• Multi-Armed Bandits
• Campaign Optimization
• Machine Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.