Kansei refers to people’s subjective feeling and impression. Kansei evaluation devotes to assessing users’ preferences for product items according to multiple Kansei attributes, thus supporting the decision making of consumers and/or designers. The objective of this paper is to propose a data-driven approach for addressing user group oriented Kansei evaluation. The approach consists of three phases. The first phase identifies the representative Kansei attributes and product samples of the product domain to gather exemplary evaluation dataset from sampled representative users. In light of the specified Kansei need and relying on the dominance-based rough set approach, the second phase constructs the collective decision table so as to further infer the collective preferential information in terms of dominance-based decision rules and Kansei importance weights. The third phase presents a two-step sequential heuristic model for characterizing users’ affective preference behavior: (1) a multicriteria classifier using dominance-based decision rules for product sorting, partly simulating the satisficing heuristic; and (2) a simple choice strategy for product ranking, manifesting the CONF heuristic. A case study involving the toaster domain was conducted to verify the proposed approach. The theoretical and practical implications of the proposed approach are also discussed.

感性是指人们的主观感觉和印象。Kansei评估致力于根据多个Kansei属性评估用户对产品项的偏好，从而支持消费者和/或设计师的决策。本文的目的是提出一种数据驱动的方法来解决面向用户组的感性评估。该方法包括三个阶段。第一阶段识别代表性的关西属性和产品领域的产品样本，以从抽样的代表性用户那里收集示例性评估数据集。根据指定的Kansei需求并依赖基于优势的粗糙集方法，第二阶段构造集体决策表，以便根据基于优势的决策规则和关西重要性权重进一步推断集体优惠信息。第三阶段提供了一个两步顺序的启发式模型，用于表征用户的情感偏好行为：（1）使用基于优势的决策规则进行产品分类的多准则分类器，部分模拟了令人满足的启发式 （2）一种用于产品排名的简单选择策略，体现了CONF启发式方法。进行了涉及烤面包机领域的案例研究，以验证所提出的方法。还讨论了该方法的理论和实践意义。