# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 4 Experimental Analyses
### Experimental Results in Similarity Based Models
#### 4.3.2 Performance Analysis  
We evaluate the effect of correcting natural noise by comparing the difference in recommendation quality between the original and denoised datasets. For all the schemes in Table 6, the evaluation metrics are calculated.  
Figures 5-7 depict the \(MAEs\) and \(RMSEs\) on the datasets Movielens-100K, Yahoo Music, and Epinions. It is easily observed from the trend of the curves that both \(MAE\) and \(RMSE\) decrease as we increase the neighbor size. Moreover, our scheme has the lowest \(MAE\) and \(RMSE\). Compared with the Original PCC, our scheme achieves an obvious promotion on the accuracy of predicted ratings. For example, in the dataset Movielens-100K, the \(MAE\) and the \(RMSE\) are improved by about 5.1% and 5.3%, respectively; in the dataset Yahoo Music, the improvements of \(MAE\) and \(RMSE\) are about 6.5% and 5.8%; in the dataset Epinions, the improvements of \(MAE\) and \(RMSE\) in are about 11.3% and 11.8%. These results indicate that our scheme has a good effect in terms of correcting noises.  
The Rpredict PCC and the Threshold PCC are two schemes with denoising technology used for comparison. Comparing with the Rpredict PCC, our scheme has obvious advantages in \(MAE\) and \(RMSE\). Moreover, since our scheme can correct the natural noise directly, it has higher efficiency than the Rpredict PCC, which needs to recalculate the ratings. Although the Threshold PCC can directly correct the natural noise in ratings and has high efficiency, it only changes the ratings from one level to another level, which probably introduces new deviation and limits the further promotion of predictive accuracy. The results in Figures 5-7 also confirm this claim. In Figure 5, the gaps of \(MAE\) and \(RMSE\) between the Threshold PCC and our scheme are about 2% and 2.4%, respectively. In Figure 6, our scheme has about 2.7% and 2.5% advantages in \(MAE\) and \(RMSE\) over the Threshold PCC. In Figure 7, our scheme still has about 1.3% and 2.3% better performances in the \(MAE\) and \(RMSE\) than the Threshold PCC. Therefore, the experimental results indicate that our scheme can effectively detect and correct natural noises.  
The \(F1\) values are calculated according to the cases listed in Table 6. The experimental results are depicted in Figures 8-10. According to Figure 8, it can be seen that our scheme and the Threshold PCC have larger \(F1\) values than those of the Original PCC and the Rpredict PCC in the dataset Movielens-100K.  
Compared with the Original PCC, the \(F1\) value of our scheme increases by about 6.7%, which means that correcting the natural noises in ratings can effectively improve the quality of recommendation. Figure 9 shows the test result in the dataset Yahoo Music. Obviously, our scheme has the best \(F1\) values among all of the schemes. Different from Figures 8 and 10, the \(F1\) value decreases as the number of neighbors increases in Figure 9. The reason may be related to the quality of ratings. In the dataset Yahoo music, about 16.72% of ratings are detected with natural noises. Meanwhile, the percentages of the ratings with natural noises in the datasets Movielens-100K and Epinions are 6.85% and 6.09%, respectively. This means that the quality of ratings in Yahoo Music is lower than the other datasets. When the quality of ratings is low, using more neighbors in the calculation of recommendation may introduce more errors. The results in Figure 10 are similar to the results in Figure 8. Our scheme and the Threshold PCC have much better \(F1\) values than the Original PCC and the Repredict PCC. Moreover, although the Threshold PCC has the best \(F1\) value in the dataset Epinions, the \(F1\) value of our scheme is very close to that of the Threshold PCC. The difference between them is smaller than 1%. Based on the above analysis on \(F1\) values, we can conclude that our scheme also improves recommendation accuracy by correcting natural noises.