Integrating object detection and natural language processing models to build a personalized attraction recommendation agent in a smart product service system

Abstract

Product Service System (PSS) is a new business model that integrates tangible products and intangible services to better meet customer needs and expectations. In recent years, scholars had some efforts to enhance the capability of PSS with the support of artificial intelligence (AI) which is known as Smart PSS(SPSS). So far, most previous studies adopted a single model which cannot handle multiple tasks simultaneously that results in unsatisfactory services to customers. In addition, customer preference cannot be fully addressed to achieve personalization. Therefore, this study proposes a method that integrates multiple models into an SPSS with three steps: (1) Collect data and construct an appropriate object detection model. (2) Develop smart PSS solutions. Then, (3) Optimize the system based on feedback through Natural Language Processing (NLP) to provide customers with personalized services. An attraction recommendation case study with experiment is designed to verify the proposed method. The results show that proposed SPSS can optimize the system in time according to the feedback of users, and provide better personalized services. This research is the first research that utilizes applied both text and image data to extract customer characteristics to better capture the voice of customers.