## 4 Experiments and Results
### Experiment 3--Performance of Personalized Recommendation  
To evaluate the performance of the proposed algorithm for personalized recommendation results, the TOP-N lists generated from 10 interactions are comprehensively evaluated using three types of evaluation metrics. This experiment uses the same dataset as Experiment 2 and compares it with the SC-DRBMIIEDA algorithm, and the two algorithms are run 30 times independently. The biggest difference between the two algorithms is that the excellent initial population of the proposed algorithm evolves further to mine preference knowledge, while the population of SC-DRBMIIEDA does not have this process. In comparison with it, the advantage of migrating inter-preference knowledge is easiest to manifest. In particular, to ensure the comparability of the two algorithms, the population size of SC-DRBMIIEDA is the same as the size of the pre-recommended list, except that the model parameters are the same as in Experiment 2. That is, the source size of the TOP-N list is the same. Figures 6-8 depict the performance of the two personalized recommendation algorithms on Music, CDs, and Kindle datasets, respectively. A summary of the combined performance of each metric on the ten interaction results can be found in Table 5, where IMFEOA and IEDA stand for MPUSMs-IMFEDA and SC-DRBMIIEDA, respectively. Best and Ave are the maximum and average of the ten interaction results. Growth represents the average percentage improvement of the proposed algorithm over SC-DRBMIIEDA, and Total denotes the average growth rates of the combined 3 datasets. Bolded values in the table indicate higher performance. In this experiment, the Mann-Whitney U nonparametric test with a confidence level of 0.95 is used to demonstrate that the proposed algorithms are significantly different from each other, and the mark \" indicates that the algorithm is significantly different from other algorithms. Figure 6: The recommendation performance of the two algorithms on six metrics when performing interactions in the Music dataset. Combining Figures 6-8, the following conclusions could be drawn. For one, the MPUSMs-IMFEOA algorithm consistently excels in \(Div\) metric, and the curve is smoother. This indicates the effective improvement of the individual diversity and its stability of recommendation results by dividing preferences through MPUSMs and selecting appropriate genetic operators. It also suggests that the algorithm improves the search ability for each preference. Second, as shown by the performance of \(SSD\), the temporal diversity of SC-DRBMIEDA decreases significantly with the reduction of the project pool size, while the proposed MPUSMs-IMFEOA is more likely to maintain temporal diversity and is less affected by the size of the project pool. Third, the proposed algorithm consistently performs  
Figure 8: The recommendation performance of the two algorithms on six metrics when performing interactions in the Kindle dataset. Figure 7: The recommendation performance of the two algorithms on six metrics when performing interactions in the CD dataset. better on the \(PMD\) metric. The significant improvement in \(PMD\) implies that the algorithm is significantly more capable of mining unknown user preferences. It indicates that knowledge transfer between preferences can effectively facilitate the discovery of entirely new items. As shown in Table 5, the MPUSMs-IMFECOA has a lower accuracy according to the Ave of \(HR\) and \(AP\) metrics. Even so, the MPUSM-IMFECOA algorithm is still sufficient to recommend desired items. This is directly reflected in the \(AP\) values above 0.9 for all three datasets, as the \(AP\) values can laterally reflect the number of satisfying items of users in the recommendation list. Furthermore, the proposed algorithm performs poorly in \(HR\) and \(AP\) metrics for two reasons. One may be due to the reduced scoring accuracy of the MPUSMs.