Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++

Algorithm 1

Hierarchical sampling statistics model for recommendation.

Input: Results of questionnaire survey (T).

Output: Recommendation list ()

1. Target random variables including travel season, travel interest, and travel method are used to

depict the user preference. Each variable is described by six population attributes namely gender,

district, age, education, job, and wage.

2. On the basis of the T, the sampling dataset is obtained by the HSS model. and the sampling

number of the i-th hierarchy is obtained. which is expressed as Equation (2).

3. The proportional value of each attribute hierarchy is calculated by /N, which can depict the

actual distribution of the corresponding attribute.

4. On the basis of the preceding proportional values, the relative importance of each attribute is

determined by the subjective weighting method, and a discriminant matrix (G) is obtained, which

is expressed as Equation (4).

5. The weight of each attribute hierarchy is calculated on the basis of the matrix G and Equation

(5), and each target random variable is described by the six weighted previously population attributes.

6. Population attributes are ranked by their weights and recommendation results are generated

according to the ranked attributes.

7. Recommendation list is generated by matching the above recommendation results and

users’ population attributes collected from the survey.