Leveraging Action Knowledge from Product Reviews to Enhance Human-Centered Recommender Systems: A Knowledge Graph-Based Framework

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.