The development of the sharing economy has given birth to a new form of experience economy called shared accommodation, which benefits from many innovations in its ability to shape “unforgettable experiences” for tourists. However, it is unclear how tourists form unforgettable experiences based on individual interactions in shared accommodation scenarios. Based on the comprehensive experience paradigm, this study systematically explores the influence mechanism of the features of shared accommodation on the tourist experience through grounded theory and further refines the formation path of tourists’ unforgettable experiences. The results show that tourists form unforgettable experiences in shared accommodation scenarios through three experiential paths: functional, emotional, and symbolic, and which correspond to the physical, psychological, and spiritual dimensions, respectively. Simultaneously, four major factors influenced the occurrence of unforgettable experiences: infrastructure functions, service experiences, interpersonal interactions, and novel experiences. These results provide a reference for the sharing economy industry to optimize “experience-based” products and services.

Most of the existing research on tourism big data in China has focused on identifying and analyzing specific problems in the tourism industry and lacks a critical evaluation of contemporary, state-of-the-art studies on big data in tourism. This paper examines empirical studies of big data in tourism that have been published in the Chinese language, reviewing 358 journal papers published in prestigious Chinese academic publications on tourism as of January 2021. In these studies, three types of big data are used: UGC data, device data, and transaction data. Under each data type, in-depth investigations of the study’s goals, data features, and analytical approaches are undertaken. While tourism big data research in China has taken significant strides in study topics, data collection, and analytic methods, the distribution of research on various forms of data is as yet uneven. There is, therefore, room for improvement in study topics, data collection methods, and analysis approaches.