Fig. 4
From: Recommendation algorithm based on user score probability and project type
Comparison of the MAE values of the four algorithms on the data set train1. When the number of neighbors is the same, the MAE value of the improved algorithm UPCF is the smallest, that is, the prediction error is the smallest and the recommendation performance is the best. When the number of neighbors is different, the MAE value of the four algorithms decreases first and then increases with the growth of the neighbors. It shows that the MAE is affected by the neighbors, in other words, the performance is affected by the neighbors