## 4 Numerical Experiments
### Experimental Results
#### 4.2.2 Eachmovie Dataset  
We calculate the Top-\(N\) accuracies for the approaches. Table 3 summarizes the Top-\(N\) accuracy for case 1, where we can effectively check the recommendation performance by manipulating the \(N\). The Top-\(N\) accuracy ranging from 0 to 1 has been widely used for evaluating the recommendation performance because the \(N\) can be selected by recommender system managers, and they are interested in how many items among the recommended ones would be actually chosen by users [18; 19; 20; 21; 22; 23; 24]. The bold numbers in the table indicate the best performances. In case 1, for the PCA+LR item modeling and PCA+LR user modeling, the PCA+LR two-way 1 performs the best for Top-8, Top-9, and Top-10, whereas the PCA+LR user modeling is the best for Top-1, Top-6, and Top-7, and the PCA+LR item modeling is the best for Top-1 to Top-5. For the user-based CF and item-based CF, the Pearson correlation-based score performs the best for Top-2, Top-3, Top-9, and Top-10, whereas the user-based CF performs the best for Top-1 and the item-based CF does for Top-4 to Top-9. For the RF item modeling and RF user modeling, the RF R-square-based score performs the best for Top-1 and Top-4 to Top-10, whereas the RF user modeling performs the best for Top-3 and the RF item modeling does for Top-1 and Top-2. For the two-way cooperative CF, the Pearson correlation-based score and the RF R-square-based score provide the best average of the ten Top-\(N\) accuracies. Therefore, we realize that the Pearson correlation-based score as well as the RF R-square-based score works more effectively than the PCA+LR two-way 1. Note that there are 604 users and 207 items in section A \(\times\) C in Figure 1a. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  &
\begin{tabular}{c} **Classification** \\ **Error** \\ \end{tabular} & **Precision** & **Recall** & **F1 Score** \\ \hline PCA+LR item modeling & \(0.273\left(\frac{267}{980}\right)\) & \(0.475\left(\frac{28}{59}\right)\) & \(0.106\left(\frac{28}{264}\right)\) & \(0.173\) \\ \hline PCA+LR user modeling & \(0.269\left(\frac{264}{980}\right)\) & \(0.500\left(\frac{18}{86}\right)\) & \(0.068\left(\frac{18}{264}\right)\) & \(0.120\) \\ \hline PCA+LR two-way 1 & NA & NA & NA & NA \\ \hline PCA+LR two-way 2 & NA & NA & NA & NA \\ \hline User-based CF & \(0.267\left(\frac{262}{980}\right)\) & \(0.583\left(\frac{7}{12}\right)\) & \(0.026\left(\frac{7}{264}\right)\) & \(0.050\) \\ \hline Item-based CF & \(0.261\left(\frac{256}{980}\right)\) & \(0.667\left(\frac{16}{24}\right)\) & \(0.060\left(\frac{16}{264}\right)\) & \(0.110\) \\ \hline Pearson correlation-based & \(0.260\left(\frac{255}{980}\right)\) & \(0.737\left(\frac{14}{19}\right)\) & \(0.053\left(\frac{14}{264}\right)\) & \(0.099\) \\ \hline RF item modeling & \(0.260\left(\frac{255}{980}\right)\) & \(0.800\left(\frac{12}{15}\right)\) & \(0.046\left(\frac{12}{264}\right)\) & \(0.087\) \\ \hline RF user modeling & \(0.260\left(\frac{255}{980}\right)\) & \(0.800\left(\frac{12}{15}\right)\) & \(0.046\left(\frac{12}{264}\right)\) & \(0.087\) \\ \hline RF R-square-based score & \(0.261\left(\frac{256}{980}\right)\) & \(0.700\left(\frac{14}{20}\right)\) & \(0.053\left(\frac{14}{264}\right)\) & \(0.099\) \\ \hline RF Pearson & \(0.259\left(\frac{254}{980}\right)\) & \(0.639\left(\frac{23}{86}\right)\) & \(0.087\left(\frac{23}{264}\right)\) & \(0.153\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction performance results. In case 2, as shown in Table 4, for the PCA+LR item modeling and PCA+LR user modeling, the PCA+LR two-way 1 performs the best for Top-2, Top-3, Top-9, and Top-10, whereas the PCA+LR user modeling is the best for Top-1 and Top-4 toTop-8 and the PCA+LR item modeling is the best for Top-1. For the user-based CF and item-based CF, the Pearson correlation-based score performs the best for Top-1 to Top-8, whereas the user-based CF performs the best for Top-2 to Top-4, Top-7, Top-9, and Top-10; the item-based CF is outperformed by the two approaches.