A proactive interaction design method for personalized user context prediction in smart-product service system

Abstract

Smart product-service system (Smart PSS), as an emerging digital servitization paradigm, has attracted strong interest from both industry and academia worldwide. Compared with traditional PSS, Smart PSS has three unique characteristics, namely context awareness, closed-loop design, and IT-driven value co-creation, which put forward higher requirements for its solution design. Specifically, it aims for proactive interaction with users, which emphasizes context-aware prediction in the targeted service scenarios other than passive responses to the users. Meanwhile, smart connected products can be empowered to become agents to recommend personalized services accordingly. Nevertheless, few studies have considered a proactive interaction design approach in the Smart PSS context. Aiming to fill the gap, this study presents a systematic method that mainly includes three parts: context awareness, interaction decision-making graph, and interaction solution recommendation. By utilizing the proposed method, Smart PSS can actively interact with users and further provide optimal solutions according to personalized context data. At last, an illustrative case study of a smart reading solution is demonstrated to show its feasibility and effectiveness.