Profit Maximization in Online Influencer Marketing From the Perspective of Modified Agent-Based Modeling

To provide a new perspective for understanding the dynamics of advertising campaigns in online influencer marketing (OIM), this article proposes the modified agent-based (MAB) model. As an extension of the existing agent-based model, it takes into account some important factors such as influencer avoidance and product competition and modifies some existing parameters to provide a more realistic approach for simulating marketing campaigns. Based on it, we define a novel set function named as dual-profit function. It is proven to be nonsubmodular and nonsupermodular. The dual-profit maximization (DPM) problem which regards dual-profit function as its objective function and the DPM algorithm used to address DPM problem is proposed. The influence of several parameters is evaluated through experiments conducted on real-world datasets.