Actions that people aim to do are considered one of the main drivers behind purchase decisions and uncovering people’s needs in a human-centered manner. Such actions are often expressed by buyers in product reviews. However, most existing recommender system approaches still lack incorporating buyer-product action knowledge in the recommendation process. This limitation increases the gap between buyers’ needs and the recommended products. This research proposes a knowledge graph-based framework to represent buyers’ action knowledge from product reviews and integrate it in recommender systems to provide more human-centered and explainable recommendations. The framework is validated through a set of prototypes, which demonstrate the feasibility of buyers expressing their needs in the form of actions and recommending products accordingly. An initial evaluation revealed a promising 75% System Usability Scale score, with interview-based feedback that shed light on the capabilities of the proposed approach in supporting buyers in their online product selection experience.

人们想要采取的行动被认为是购买决策背后的主要驱动力之一，并以以人为本的方式揭示人们的需求。此类行为通常由买家在产品评论中表达。然而，大多数现有的推荐系统方法仍然缺乏在推荐过程中纳入买家-产品行为知识。这种限制增加了买家需求与推荐产品之间的差距。这项研究提出了一个基于知识图的框架，来表示买家从产品评论中获得的行动知识，并将其集成到推荐系统中，以提供更加以人为本和可解释的推荐。该框架通过一组原型进行了验证，证明了买家以行动的形式表达需求并相应推荐产品的可行性。初步评估显示，系统可用性量表得分高达 75%，基于访谈的反馈揭示了所提议的方法在支持买家在线产品选择体验方面的能力。