analiza aiqbench
This research addresses the optimization of marketing budgets within social networks by modeling heterogeneous user responsiveness and network spillovers, a core problem in algorithmic decision-making for AI-driven advertising systems. The authors propose a scalable greedy procedure with provable approximation guarantees that outperforms standard heuristics when allocating continuous spend levels across large-scale graphs. While the application is commercial, the underlying methodology relies on advanced machine learning optimization techniques and graph algorithms central to modern AI product development.
Optimizing Marketing Budgets for Social Influence: A Generalized Fractional Incentives Framework
arXiv:2610.25823v1 Announce Type: cross Abstract: Marketing managers routinely allocate limited budgets across individuals in social networks whose responsiveness and influence vary substantially. We study the problem of Budgeted Generalized Fractional Influence Maximization (BGFIM), which captures the allocation of continuous levels of marketing spend across users in the presence of heterogeneous response and network spillovers. In this framework, adoption probabilities are governed by user-specific response functions that capture both baseline propensity and marginal responsiveness, while incentive costs vary across users. We propose a scalable solution approach based on a discrete greedy procedure with provable approximation guarantees, enabling tractable optimization under realistic budget constraints. Our formulation generalizes prior influence maximization models by allowing heterogeneous response and cost structures, providing a more flexible representation of targeted marketing interventions. Using experiments on multiple large-scale networks, we show that continuous allocation strategies can outperform standard heuristics and improve influence outcomes under heterogeneous conditions, and we illustrate how the framework can support profit-oriented decision making by identifying budget levels that balance marginal returns and costs. Overall, our results highlight the value of fine-grained budget allocation in networked marketing environments and offer a scalable approach for data-driven marketing resource allocation.
Abstract:Marketing managers routinely allocate limited budgets across individuals in social networks whose responsiveness and influence vary substantially. We study the problem of Budgeted Generalized Fractional Influence Maximization (BGFIM), which captures the allocation of continuous levels of marketing spend across users in the presence of heterogeneous response and network spillovers. In this framework, adoption probabilities are governed by user-specific response functions that capture both baseline propensity and marginal responsiveness, while incentive costs vary across users. We propose a scalable solution approach based on a discrete greedy procedure with provable approximation guarantees, enabling tractable optimization under realistic budget constraints. Our formulation generalizes prior influence maximization models by allowing heterogeneous response and cost structures, providing a more flexible representation of targeted marketing interventions. Using experiments on multiple large-scale networks, we show that continuous allocation strategies can outperform standard heuristics and improve influence outcomes under heterogeneous conditions, and we illustrate how the framework can support profit-oriented decision making by identifying budget levels that balance marginal returns and costs. Overall, our results highlight the value of fine-grained budget allocation in networked marketing environments and offer a scalable approach for data-driven marketing resource allocation.
Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI)
F.2.2; G.1.2; G.1.6; G.2.1; G.2.2; G.3; I.2.0; J.4
arXiv:2610.25823 [cs.SI]
(or arXiv:2610.25823v1 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2610.25823 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 295 cuvinte
Subiecte conexe
p-PSO: A Penalized Particle Swarm Optimization Technique for Finding D-Optimal Designs with Mixed Factors in Generalized Linear Models
arXiv · 16 iun. 2026
Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective
arXiv · 21 iul. 2026
One-Step Generalization Ratio Guided Optimization for Domain Generalization
arXiv · 16 iun. 2026
Balancing multiscale similarity and cartographic constraints: A similarity-driven optimization framework for line generalization
arXiv · 29 iul. 2026