Developing a personalized recommendation system in a smart product service system based on unsupervised learning model

Contemporary consumers have begun shifting their focus from product functionality toward the value that can be derived from products. In response to this trend, companies have begun using product service systems (PSS), business models that provide customers not only with tangible products but also with intangible services. Moreover, with the increasing use of smart devices, services providers can offer customized services to customers based on user-generated data with smart product service systems (Smart PSS). Despite extensive research on Smart PSS framework, few of these frameworks treated customer as an active data producer, which means producing data for the Smart PSS actively. Additionally, most of them proposed a general solution instead of a personalized one. To bridge the research gap, this study proposed a method that includes: (1) unsupervised natural language processing (NLP) methods to analyze user-provided data. (2) a recommendation system integrating deep learning to offer customers with personalized solutions. Thus, the role of customers is not only a service receiver but also an active data producer and forms a value co-creation process with service providers. A case study of tourist recommendation validate the benefits of proposed method. The main contribution of this research is to develop a personalized smart PSS method which could achieve a win-win situation for all players in this method. (c) 2021 Elsevier B.V. All rights reserved.