Recommendation algorithm based on user score probability and project type

EURASIP Journal on Wireless Communications and Networking

Table 1 Comparison table for MAE value

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