Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing
arXiv:2509.22557v3 Announce Type: replace Abstract: Mixed bundle pricing is a classic revenue management problem arising in industries such as e-commerce, tourism, and video games. It refers to designing product combinations (i.e., bundles) and determining their prices to maximize expected profit. Exact mixed-bundling formulations capture this structure but are computationally intractable because the number of possible bundles grows exponentially with the number of products. We propose a graph neural network (GNN)-guided pruning framework for scalable (non-)additive bundle pricing. Instead of
The increasing complexity of e-commerce and digital marketplaces necessitates more scalable and efficient pricing strategies to handle vast numbers of product combinations.
This development allows businesses in e-commerce, tourism, and gaming to optimize revenue more effectively by overcoming computational bottlenecks in bundling, directly impacting profitability and market efficiency.
Traditional computationally intractable mixed bundle pricing models can now be scaled through a GNN-guided pruning framework, expanding the practical application of advanced pricing strategies.
- · E-commerce platforms
- · Online travel agencies
- · Video game publishers
- · AI/ML solution providers
- · Companies relying on static pricing
- · Legacy revenue management software
Companies can implement more dynamic and complex product bundling strategies at scale, leading to increased revenue.
Enhanced precision in pricing could intensify competition among businesses by allowing for more granular market segmentation and personalized offers.
The widespread adoption of such AI-driven pricing systems might lead to new regulatory scrutiny regarding algorithmic fairness and potential price discrimination.
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Read at arXiv cs.LG