## 4 Experiments and Evaluation
### Experimental Results and Analysis
From Tables 6-9, we can conclude that, for precision and recall metrics, the proposed IM-IBCF gives higher values than IBCF in both MovieLens 100K and MovieLens 1M datasets. Because higher values indicate better accuracy performance, it implies that the proposed approach can predict rating scores for new items more accurately. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\#Recommended**} & \multirow{2}{*}{**Recommendation**} & \multicolumn{8}{c}{**\#Neighborhood**} \\ \cline{3-10}  & & \(L=20\) & \(L=25\) & \(L=30\) & \(L=35\) & \(L=40\) & \(L=45\) & \(L=50\) & \(L=55\) & \(L=60\) \\ \hline \multirow{3}{*}{\(N=2\)} & IM-IBCF & **0.478** & 0.479 & 0.480 & **0.476** & **0.473** & **0.468** & **0.462** & **0.473** & **0.500** \\  & IBCF & 0.474 & **0.524** & **0.526** & 0.475 & 0.417 & 0.434 & 0.450 & 0.447 & 0.444 \\  & IM-IBCF & 0.261 & 0.266 & 0.270 & **0.274** & **0.278** & **0.273** & **0.270** & **0.273** & **0.276** \\  & IBCF & **0.273** & **0.286** & **0.289** & 0.255 & 0.236 & 0.233 & 0.237 & 0.229 & 0.222 \\  & IM-IBCF & 0.196 & 0.199 & 0.209 & 0.211 & 0.214 & **0.207** & **0.204** & **0.198** & **0.191** \\  & IBCF & **0.234** & **0.221** & **0.222** & **0.219** & **0.216** & 0.201 & 0.193 & 0.189 & 0.186 \\  & IM-IBCF & 0.164 & **0.197** & **0.187** & 0.164 & **0.203** & **0.195** & **0.188** & **0.186** & **0.185** \\  & IBCF & **0.207** & 0.166 & 0.169 & **0.201** & 0.161 & 0.157 & 0.153 & 0.147 & 0.141 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of precision measure on the MovieLens 100K dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\#Recommended**} & \multirow{2}{*}{**Recommendation**} & \multicolumn{8}{c}{**\#Neighborhood**} \\ \cline{3-10}  & & \(L=20\) & \(L=25\) & \(L=30\) & \(L=35\) & \(L=40\) & \(L=45\) & \(L=50\) & \(L=55\) & \(L=60\) \\ \hline \multirow{3}{*}{\(N=2\)} & IM-IBCF & **0.235** & 0.238 & 0.237 & **0.242** & 0.239 & **0.235** & **0.233** & **0.236** & **0.241** \\  & IBCF & 0.231 & **0.239** & **0.238** & 0.239 & **0.240** & 0.232 & 0.230 & 0.232 & 0.239 \\  & IM-IBCF & **0.251** & 0.249 & 0.251 & 0.258 & **0.263** & **0.265** & **0.261** & **0.269** & **0.273** \\  & IBCF & 0.246 & **0.257** & **0.259** & **0.262** & 0.257 & 0.256 & 0.254 & 0.259 & 0.265 \\  & IM-IBCF & **0.273** & 0.264 & 0.271 & **0.281** & **0.278** & 0.279 & **0.284** & **0.285** & **0.289** \\  & IBCF & 0.268 & **0.269** & **0.279** & 0.277 & 0.273 & **0.280** & 0.281 & 0.283 & 0.287 \\  & IM-IBCF & **0.286** & 0.283 & **0.290** & 0.286 & **0.297** & **0.311** & **0.308** & **0.307** & **0.303** \\  & IBCF & 0.281 & **0.287** & 0.285 & **0.288** & 0.290 & 0.293 & 0.297 & 0.304 & 0.301 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of recall measure on the MovieLens 100K dataset. Figure 2 shows the performance of MP and MN for different sizes of neighborhoods on the MovieLens 100K dataset. From the figures, we can observe that increasing the size of the neighborhood would result in higher MP values for both approaches. However, the proposed IM-IBCF produces a better performance than IBCF. For MN metric, the proposed IM-IBCF surpasses IBCF in the whole range of size of neighborhoods. Figure 3 presents comparison of MP and MN results obtained using the MovieLens 1M dataset. As shown in the figures, MP values of IM-IBCF has no remarkable change in the whole size of the neighborhood range; however, IBCF increases clearly as the size of neighborhood increases. However, the proposed IM-IBCF can surpass IBCF over all ranges of neighborhoods. For MN metric, increasing the size of neighborhood results in an increase in MN values of the two approaches. Further, the proposed IM-IBCF approach surpasses IBCF for the whole range of neighborhoods. From Figures 2 and  
Experimental results above show that the proposed approach has better accuracy and diversity than IBCF on the CNICS problem.