Hidden semi-Markov model-based reputation management system for online to offline (O2O) e-commerce markets

The rapid development of information technology enables an increasing number of consumers to search and book products/services online first and then to consume them in brick-and-mortar stores. This new e-commerce model is called online to offline (O2O) e-commerce and has received significant managerial and academic attention. Compared with many extant e-commerce models (i.e., B2B, B2C and C2C), reputation management in this emerging model needs some improvement. It has to collect more raw reputation-related data, consider more reputation-related factors and show more comprehensive reputation evaluation results. As a stepping-stone in the research in O2O e-commerce, a new reputation management system (HSMM-RMS) has been developed based on a probabilistic model called the hidden semi-Markov model. By combining observable online and offline raw reputation information, the proposed system can accurately, promptly and dynamically provide O2O e-commerce participants with offline merchants' historical and predictive reputation information. Our Monte-Carlo simulation experiments indicate that the proposed system performs significantly better than the extant hidden Markov model-based reputation management system. A case study based on a real O2O e-commerce platform demonstrates the real application of HSMM-RMS. It also shows that the proposed system can provide a realistic solution for reputation management in the O2O e-commerce market. A new system is built to manage reputation of offline merchants in O2O ecommerce.The proposed reputation management system is based hidden semi-Markov model.This system can generate both historical and predictive reputation information.The Monte-Carlo simulations and real case study illustrate the usefulness and real application of our system.

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