## 4 Experiments and Evaluation
### Experimental Results and Analysis  
We conducted experiments to evaluate the performance of our proposed approach which applies interrelationship mining to IBCF as IM-IBCF. In addition, we compared the results obtained with that of IBCF. In all the experiments conducted, we employed JAC measure to compute similarity, and weighted sum approach was used to predict the rating scores. Finally, we selected the top \(N\) candidate items with highest predicted rating scores as the recommendations. Tables 6 and 7 show a comparison of the precision and recall results of the mentioned recommendation approaches on MovieLens 100K dataset. Moreover, the tables also show comparative results for a different number of neighborhoods and recommended items. It should be noted that we grouped experimental data according to the number of recommended items and neighborhoods (e.g., experimental results in \(N=2\) and \(L=\)20 will be a group), the best result of each group is boldfaced, and the best performance among all approaches in each table is underlined. From the results, it is clear that the highest precision and recall results are obtained for different numbers of recommended items and neighborhood. For example, if the number of recommended items is set to 2, the best result of precision measure is 0.500 when the number of neighborhoods is 60, and the best recall value is 0.242 when the number of neighborhood is set to 35. Moreover, when the number of neighborhoods is fixed, values of precision for each mentioned approach decrease as the number of recommended items increases; on the contrary, recall result increases as the number of recommended items increases. In addition, it can be concluded from each table results that precision and recall gain their best results for a different number of recommended items and neighborhood. For example, for the precision metric,  
the best value was obtained when \(N=2\) and \(L=60\); however, the best recall result was obtained when \(N=8\) and \(L=45\). According to the results in Tables 6 and 7, we can conclude that although values of precision and recall of the proposed IM-IBCF approach are worse than the one obtained using IBCF when the number of neighborhoods is less than 45, IM-IBCF approach outperforms IBCF as the number of neighborhood increases. Furthermore, compared with IBCF, IM-IBCF can be used to obtain the best precision and recall results when \(L=60\), \(N=2\) and \(L=45\), \(N=8\), respectively. Therefore, the proposed IM-IBCF can provide more accurate recommendation than IBCF when used on MovieLens 100K dataset. Tables 8 and 9 indicate the precision and recall results with a different number of recommended items and neighborhoods on the MovieLens 1M dataset. According to the results, we can conclude that the proposed IM-IBCF can be used to obtain the best precision results when \(N=2\) and \(L=35\). And best performance of the recall metric can also be obtained using the proposed IM-IBCF approach when \(N=8\) and \(L=40\). Moreover, it can be concluded that higher precision and recall values among the above mentioned two approaches are obtained for different numbers of recommended items and neighborhood. For example, when the number of recommended items is set to 2 (i.e., \(N=2\)), the highest precision value reported is 0.379 for the IM-IBCF approach when the number of neighborhoods is 35; however, if the number of recommended items is set to 8, the best precision result obtained is 0.191 when the number of neighborhoods is 30 (i.e., \(L=30\)). Similarly, for the recall measure, if the number of neighborhood is set to 20 (i.e., \(L=20\)), the best value 0.178 can be obtained using the IBCF approach when the number of recommended items is 8; however, if the number of neighborhood is 30 (i.e., \(L=30\)), the highest recall result obtained using the proposed IM-IBCF approach is 0.194 when the number of recommended items is set to 8.