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Table 1 Comparison table for MAE value

From: Recommendation algorithm based on user score probability and project type

  Number of neighbors
10 20 30 40 50 60 70 80
UBCF 0.792 0.777 0.773 0.772 0.772 0.772 0.772 0.773
IBCF 0.863 0.839 0.829 0.824 0.820 0.818 0.817 0.815
GSCF 0.779 0.767 0.763 0.763 0.763 0.764 0.764 0.765
UPCF 0.770 0.761 0.758 0.757 0.757 0.758 0.759 0.762
  1. 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