Intelligent Algorithm of Tourist Attraction Recommendation Based on Big Data

Technological reform and innovation will promote the development and construction of tourism, and provide us with more convenient service experience. With information overload, we need new methods to provide better services for users. Personalized recommendation is one of the key directions of smart tourism research. The purpose of this paper is to study the intelligent algorithm of tourist attraction recommendation based on big data. By calculating the heat vector of scenic spots and adding the time context, the BIPM personalized recommendation algorithm proposed in this paper is adopted to recommend scenic spots for users. The recommendation model is verified and analyzed by using the data set captured from the tourism website platform. The results show that the proposed BIPM algorithm is superior to the collaborative filtering algorithm.