Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
Researchers propose a novel Profit-Based Counterfactual Explanation (PBCE) framework that integrates machine learning with business profit maximization, demonstrated through a case study on Japanese manga sales.
Computer Science > Artificial Intelligence
Title:Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
View PDF HTML (experimental)Abstract:Counterfactual explanation (CE) is widely used to enhance the interpretability of machine learning models and support data-driven decision-making based on model predictions. However, existing CE methods typically require two exogenously specified inputs: a desired output value (target) and a distance function that quantifies changes in explanatory variables. In regression settings, neither the validity of target specification nor the practical interpretation of the distance metric has been sufficiently addressed. Furthermore, most existing CE methods focus on altering predictions rather than optimizing a decision objective, even though real-world decision-making often requires explicit objective maximization. To address these limitations, we formulate CE as a profit maximization problem in management and marketing contexts and propose a framework termed profit-based counterfactual explanation (PBCE). PBCE eliminates the need for exogenous target specification by directly maximizing profit as the primary optimization objective. Concurrently, the distance term is reinterpreted as the cost of modifying product attributes, providing a clear and economically grounded interpretation.
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Source: arXiv cs.AI Recent
