Fig. 6
From: Recommendation algorithm based on user score probability and project type
The comparison of the MAE values between four algorithms in data set of train2. The MAE value of the three algorithms decreases first and then increases. When the nearest neighbor number is about 40, the MAE value is the smallest, which shows that the collaborative filtering algorithm is affected by the nearest neighbor number. When the nearest neighbor number is the same, the MAE of UPCF algorithm is the smallest, that is, the predicted score is close to the real value of the user and it provides the best recommendation result. It fully illustrates the importance of the user to the subjective behavior of the commodity score