An ontology-based product usage context modeling method for smart customization

Abstract

Product usage context (PUC) identification is an effective approach to approximate the complex driver behind heterogeneous customer preference. With the sweeping trend of data-driven smart customization, a large volume of product usage data has allowed designers to understand contextual customer needs (CNs) and enable them to offer highly customized products and services in time. However, as the PUC ontology is not clearly defined, most of the existing PUC models are incomplete, ambiguous and imprecise. Inappropriate use of those models will result in failing to extract knowledge from data. In this paper, an ontology-based context modeling method is proposed, with the aim to help designers understand PUC in a comprehensive manner. A case study of robot vacuum cleaner (RVC) is used as an illustrative example. It is concluded that the proposed method can enable designers quickly establish a well-defined PUC model to support smart customization.