Fig. 9
From: Recommendation algorithm based on user score probability and project type
Comparison of RMSE values of two improved algorithms in a data set of train1. With the near neighbor number as the variable, the RMSE values of the two kinds of recommendation algorithms decrease first and then increase, and finally tend to be gentle. When the near neighbor number reaches about 40, the RMSE value of the two algorithms is the smallest, that is, the algorithm has the least error and the highest precision. In the case of the same number of near neighbors, the RMSE value of the algorithm UPCF is lower than that of the algorithm GSCF, that is, the error is smaller