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

Algorithm 2

Proposed SVD++ based collaborative filtering model.

Input: Users’ rating matrix (R)

Output: Rating prediction matrix (D) and Recommendation list ()

1. Compute the mean rating based on the matrix R.

2. Initialize the “bias information” b_u and b_z. Initialize the user vector p_u. Initialize the tourist spot

vector . Initialize the implicit parameters .

3. User ratings are grouped by user ids.

4. Compute the inner product of the user vector and the tourist spot vector by Equation (11).

User’s ratings are predicted.

5. Compute the prediction error based on the real rating and the predicted rating. As shown in

Equation (13)~(18), the SGD method is utilized to complete optimization.

6. Repeat the third step and fourth step to get the prediction rating . Update the rating

prediction matrix D.

7. Generate the recommendation list based on the matrix D.