## 4 Experiments and Evaluation
### Experimental Setup and the Evaluation Metrics  
Here, we used two popular real-world datasets that are often used in evaluating RSs. One is the MovieLens 100K dataset [30], which contains 1,682 movies, 943 users, and a total of 100,000 ratings. The other dataset is the MovieLens 1M dataset that contains a total of 1,000,209 ratings of 3900 movies from 6040 users. In both of the two datasets, the ratings are on a \(\{1,2,3,4,5\}\) scale, and each user rated at least 20 movies, with each movie having the following attributes: Action, Adventure, Animation, Children's, Comedy, Crime, Documentary, Drama, Fantasy, Film-Noir, Horror, Musical, Mystery, Romance, Sci-Fi, Thriller, War, and Western. In the experiment, for each dataset, we randomly selected 20% items as test items, and the remaining 80% items were treated as the training items. To ensure that each item in the test items is a new item which has no rating records, all the ratings in each test item were masked. We utilized ratings of the training items to predict rating scores for the test items, and the test items were used to evaluate the performance of the proposed approach. Note that, in order to avoid the impact of overfitting problem, we repeat the experiments 10 times for each dataset and compute the average values as our experimental results. To measure the performance of the proposed approach, we used precision and recall metrics to represent the accuracy of the recommendations. In addition, we used mean personality (MP) and mean novelty (MN) to evaluate the diversity of the recommendations. In accordance with Herlocker's research [31], to maintain real-time performance, we selected different sized \(L\) neighborhoods from candidate neighbors, \(L\epsilon\) {20, 25, 30,..., 60}. Furthermore, to calculate the precision and recall, we treated items rated not less than three as relevant items, and the number of recommendations was set to 2, 4, 6, and 8. Precision means the proportion of relevant recommended items from the total number of recommended items for the target user. Further, higher precision values indicate better performance. Suppose that \(N_{s}\) is the number of recommended items for a target user, \(N_{rs}\) denotes the number of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(at_{1}\)\(>_{1}\)\(at_{2}\) & \(at_{1}\)\(>_{1}\)\(at_{2}\) & \(at_{1}\)\(>_{1}\)\(at_{2}\) & \(at_{1}\)\(=_{eq}at_{2}\) & \(at_{1}\)\(=_{eq}at_{2}\) & \(at_{1}\)\(=_{eq}at_{2}\) & \(at_{1}\)\(<_{mo}at_{2}\) & \(at_{1}\)\(<_{mo}at_{2}\) \\ \hline \(i_{1}\) & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(i_{2}\) & 0 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 \\ \(i_{3}\) & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \(i_{4}\) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ \(i_{5}\) & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 \\ \(i_{ti1}\) & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 0 \\ \(i_{ti2}\) & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: Example of item interrelated attribute information table. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(i_{1}\) & \(i_{2}\) & \(i_{3}\) & \(i_{4}\) & \(i_{5}\) & \(t_{ti1}\) & \(i_{ti2}\) \\ \hline \(i_{1}\) & 1 & 0 & 0.25 & 0 & 0 & 0 & 0.25 \\ \(i_{2}\) & 0 & 1 & 0.20 & 0 & 0 & 0.20 & 0.20 \\ \(i_{3}\) & 0.25 & 0.20 & 1 & 0.25 & 0 & 0 & 0 \\ \(i_{4}\) & 0 & 0 & 0.25 & 1 & 0 & 0.25 & 0 \\ \(i_{5}\) & 0 & 0 & 0 & 0 & 1 & 0.25 & 0.25 \\ \(i_{ti1}\) & 0 & 0.20 & 0 & 0.25 & 0.25 & 1 & 0 \\ \(i_{ti2}\) & 0.25 & 0.20 & 0 & 0 & 0 items the target user likes that appear in the recommended list. The precision metric can be expressed as follows:  
\[Precision=\frac{N_{rs}}{N_{s}}. \tag{16}\]  
Recall indicates the proportion of relevant recommended items from all relevant items for the target user. Similar to precision, higher recall values indicate better performance. Herein, \(N_{r}\) denotes the number of items preferred by the target user. The recall metric can be computed as follows:  
\[Recall=\frac{N_{rs}}{N_{r}}. \tag{17}\]  
MP indicates the average degree of overlap between every two users' recommendations.