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.

当代消费者已经开始将其关注点从产品功能转向可以从产品中获得的价值。为了响应这种趋势，公司开始使用产品服务系统（PSS），这种业务模型不仅为客户提供有形产品，而且还为他们提供无形服务。此外，随着智能设备的使用不断增加，服务提供商可以使用智能产品服务系统（Smart PSS）根据用户生成的数据为客户提供定制服务。

尽管对Smart PSS框架进行了广泛的研究，但这些框架中很少有人将客户视为活跃的数据生产者，这意味着要主动为Smart PSS生成数据。此外，他们中的大多数人提出了一种通用的解决方案，而不是个性化的解决方案。为了缩小研究差距，本研究提出了一种方法，该方法包括：（1）用于分析用户提供的数据的无监督自然语言处理（NLP）方法。（2）整合深度学习的推荐系统，为客户提供个性化解决方案。因此，客户的角色不仅是服务接收者，而且是活跃的数据生产者，并与服务提供者形成了价值共同创造过程。以旅游推荐为例，验证了所提方法的优越性。