Argument-Based Evaluation for AI Recommendations in E-Commerce Decision Support: A Toulmin–FCM Approach

Artificial intelligence (AI) is increasingly being used to generate recommendations for e-commerce decision support. Existing XAI evaluation typically focuses on explanation attributes or user outcomes such as clarity, usefulness, trust, and acceptance but provides less insight into how an explanation justifies the recommendation itself. This study develops an argument-based evaluation framework by integrating Toulmin’s model of argumentation with fuzzy cognitive maps (FCMs). Toulmin’s model structures explanations through claim, ground, warrant, backing, qualifier, and rebuttal, while FCMs quantify the dependencies among these components and their combined support for a recommendation. The framework is applied to feature-based, example-based, and model-based explanations in an AI-assisted hotel-pricing scenario. The three explanation stimuli produce different argumentative profiles and Claim-support levels. A separate experiment using acceptance and trust as external outcomes shows a consistent pattern. The study contributes to XAI research by introducing argument-based evaluation, operationalizing argumentative structure quantitatively, and providing a common basis for comparing different explanation forms.