A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Algorithm 1

The EM algorithm of initial ratings prediction.

  Input: Ratings in training set R, Gaussian mixture distribution parameter posterior probability p, , , and k.

  Output: The probabilities of cluster partition and estimated matrix .

1  Convert ratings into preferences of item attributes based on Eq. (28).

2  Initialize model parameters: α, , , and k.

3  repeat

4    for   do

5      Calculate the posterior probability that user belongs to group based on Eq. (32).

6     end for

7     for   do

8      Calculate new posterior probabilities based on Eq. (33).

9      Calculate new mean vectors based on Eq. (34).

10      Calculate new covariance matrices based on Eq. (35).

11     end for

12  update model parameters p, , .

13  until  convergence

14  Mark cluster label probability according to

15  Predict unknown ratings based on .

16  Reset the unreliable ratings to zeroes based on Eq. (38).

17  return  .