Research on the integration path of cultural creative industry and tourism industry based on collaborative filtering recommendation algorithm

Abstract

Abstract In order to increase regional economic benefits and attract more tourists for local economic development. This paper adopts different ways to evolve industrial integration, enrich and expand the theoretical system of the industrial economy, establish new tourism strategies to meet personalized and diversified needs and make the tourism sector achieve industrial integration. Useful data are selected to filter cultural and creative, and tourism industry information by mining hidden information of coding sequences and finding support data items. Using collaborative filtering recommendation method to obtain the preference degree of fused data items, populate the data according to behavioral speculation, calculate the utility matrix similarity, and clarify the development of industry fusion path in the context of big data. The results show that the average error rate after fusion does not exceed 4%, and the fusion path of cultural and creative industries and the tourism industry improve the regional economy and the overall local living conditions.