# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 4 Experimental Analyses
### Experimental Results in Similarity Based Models
#### 4.3.1 Correction of Natural Noises  
According to the rules in Section 3.1.1, the users are divided into three subcategories: Negative, Average, and Positive. The items are also divided in three subcategories: No-Preferred, Av-Preferred, and Preferred. Figure 2a shows the results of the classifications of users and items in the dataset Movielens-100K. There are 58 Negative users, 85 Average users, and 785 Positive users in the training dataset. Meanwhile, the numbers of No-Preferred items, Av-Preferred items, and Preferred items are 285, 388, and 813, respectively. Based on the proposed rules for detecting and correcting natural noise, the natural noises are found in 6.85% of ratings (i.e., 5481 ratings). Figure 2b illustrates the corrections for natural noise in the Movielens-100K dataset. Among the 5481 ratings, 5265 ratings are corrected from low ratings to medium ratings, and 216 ratings are corrected from high ratings to medium ratings.  
Figure 3a shows the classification of users and items in the dataset Yahoo Music. In the user set, 5012 users are the Negative users, 413 users are the Average users, and 2388 users are the Positive users. Moreover, no items are regarded as No-Preferred items. In total, 877 items are regarded as Av-Preferred items, and 118 items are regarded as Preferred items. Figure 3b shows the details of correcting natural noises in the dataset Yahoo Music. About 16.72% of ratings (i.e., 14,523 ratings) are affected by the natural noise. With our method, 2080 low ratings are corrected as medium ratings, and 12,443 high ratings are corrected as medium ratings.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scheme** & **Description** \\ \hline Original PCC/ConsisRec/MF & Apply PCC/ConsisRec/MF to the original dataset \\ \multicolumn{1}{l}{Repredict PCC/ConsisRec/MF} & Apply PCC/ConsisRec/MF to the dataset which is denoised by the scheme in [14] \\ \multicolumn{1}{l}{Threshold PCC/ConsisRec/MF} & Apply PCC/ConsisRec/MF to the dataset which is denoised by the scheme in [20] \\ \multicolumn{1}{l}{Proposed PCC/ConsisRec/MF} & Apply PCC/ConsisRec/MF to the dataset which is denoised by our scheme \\ \hline \hline \end{tabular}
\end{table}
Table 6: The schemes in our experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Ratings** & **Users** & **Items** & **Sparsity** & **Rating Scale** \\ \hline Movielens-100K & 100,000 & 943 & 1682 & 6.30\% & 1–5 \\ Yahoo Music & 270,121 & 8089 & 1000 & 3.33\% & 1–5 \\ Epinions & 482,850 & 8693 & 123,330 & 0.45\% & 1–5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The details about the experimental datasets.  
The classification results for the users and the items in the dataset Epinions are shown in Figure 4a. The numbers of Negative users, Average users, and Positive users are 69, 25, and 8565, respectively. Correspondingly, the numbers of No-Preferred items, Av-Preferred items, and Preferred items are 9477, 9211, and 82,840. Figure 4b shows the results of correcting natural noises. About 6.09% of ratings (i.e., 23,532 ratings) are considered to be affected by natural noises. Here, 23,494 low ratings and 38 high ratings are corrected as medium ratings.  
Figure 4: The results of denoise on Epinions: (**a**) The classification results of users/items based on our scheme; (**b**) The correction results on noisy ratings based on our scheme.  
Figure 3: The results on Yahoo Music: (**a**) The classification results of users/items based on our scheme; (**b**) The correction results on noisy ratings based on our scheme.  
Figure 2: The results on Movielens-100K: (**a**) The classification results of users/items based on our scheme; (**b**) The correction results on noisy ratings based on our scheme.