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

Abstract

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