Rural tourism service design based on collaborative filtering algorithm after epidemic normalization

Abstract In the epidemic, normalized tertiary and rural tourism service industries are in economic depression stage. Under combined with big data technology to improve income development scale of services become current trend industry. This paper first proposes a collaborative filtering recommendation algorithm based on study design under new epidemic normalization. Then basic principle content-based is obtain interests tourists their historical behaviors recommend similar interest preferences, choosing appropriate similarity function can accuracy neighborhood-based CF method. Finally, meet tourists’ demand for full range experience build system, psychological consumption analyzed algorithm. The results show that among main factors attracting tourists, 75.54% natural scenery, 54.68% folk culture, 51.08% unique flavors food, 43.17 experiencing life, 41.73% promoting relationships friends. plays an important role accelerating revitalization by urban back countryside driving transfer consumer groups economy increase income; thus, revitalization.