Online customer data provides valuable information for product design and marketing research, as it can reveal the preferences of customers. However, analyzing these data using artificial intelligence (AI) for data-driven design is a challenging task due to potential concealed patterns. Moreover, in these research areas, most studies are only limited to finding customers' needs. In this study, we propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. The method first uses a genetic algorithm to select, rank, and combine product features that can maximize customer satisfaction based on online ratings. Then, we use SHAP (SHapley Additive exPlanations), a game theory method that assigns a value to each feature based on its contribution to the prediction, to provide a guideline for assessing the importance of each feature for the total satisfaction. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results. Our approach tackles a major challenge in the field of multi-criteria decision making and can help product designers and marketers, to understand customer preferences better with less data and effort. The proposed method outperforms benchmark methods in terms of relevant performance metrics.

翻译：在线客户数据为产品设计和市场研究提供了宝贵信息，因为它能够揭示客户的偏好。然而，利用人工智能（AI）对这些数据进行数据驱动设计分析是一项具有挑战性的任务，因为其中可能隐藏着潜在的模式。此外，在这些研究领域中，大多数研究仅限于发现客户需求。在本研究中，我们提出了一种基于博弈论的机器学习（ML）方法，用于提取产品开发的全面设计启示。该方法首先使用遗传算法来选择、排序和组合能够基于在线评分最大化客户满意度的产品特征。然后，我们使用SHAP（SHapley Additive exPlanations）——一种博弈论方法，它为每个特征分配一个基于其对预测贡献的值——来提供评估每个特征对总体满意度重要性的指南。我们将该方法应用于Kaggle上的真实笔记本电脑数据集，并根据结果得出设计启示。我们的方法解决了多准则决策领域的一个主要挑战，能够帮助产品设计师和营销人员以更少的数据和精力更好地理解客户偏好。在相关性能指标上，所提方法优于基准方法。