Tourism destination recommendation system and method based on tourism big data

[0056] Example 1: The collection of the number of different travel users is K={K1, K2, K3}={3, 2, 1}, and the collection of the first group of users who travel together in the past travels to determine the destination set is: N={N1, N2, N3}={8, 10, 5}, when a random user decides the destination, the set of people who agree to the decision is M={M1, M2, M3}={3, 2, 5}, According to the formula The set of intervention degrees for the user to make the final decision on the selected destination is W={W1, W2, W3}={1.16, 1.45, 0.72}, and the second user is selected as the decisive object of the tourist destination, and the highest intervention degree is Wmax=1.45, the similarity set between the comprehensive features of the destination determined by the second user in the past and the destination to be recommended is S={S1, S2, S3}={0.5, 0.8, 0.7}. The set of autonomous capability requirement levels of the recommended destination is A={A1, A2, A3}={4, 5, 6}, and the set of autonomous