## 5 Discussion and Conclusions  
In this article, we introduce the knowledge transfer method into the interactive evolutionary algorithm for the first time to improve the diversity and novelty of personalized recommendations. With the aim of this, a new IEA (MPUSMs-IMFECOA) is proposed based on a modified MFEA and deep learning-driven user preference surrogate models. Firstly, to describe preference information from different perspectives, MPUSMs and partial-MPUSMs are first developed. They are used to evaluate unknown items at different population evolutionary stages, and partial-MPUSMs are the objectives to be simultaneously optimized by MFEA for achieving knowledge transfer between preferences. Then, probability models of MPUSMs are constructed to generate initial populations with skill factors to improve the evolutionary efficiency of MFEA. Secondly, a new IEA method based on MPUSMs and its derived models and joint multitasking learning is proposed. Accordingly, improved MFEA, recommendation lists generation method, and interaction-assisted model management method are designed for the characteristics of the models mentioned above. Finally, comprehensive empirical studies are conducted in the Amazon dataset. Experimental results show that the proposed algorithm can increase diversity and novelty while maintaining accuracy, which demonstrates the effectiveness of building multiple surrogate models to facilitate the mining of inter-preference knowledge. Specifically, the MPUSMs ensure the accuracy of its evaluation results by conforming to the characteristic that users rely only on partial preferences to evaluate items. In addition, with a loss of only about 5% recommendation accuracy (\(HR\) and \(AP\)), individual diversity (\(Div\)) improved by 54.02%, temporal diversity (\(SSD\)) by 3.7%, surprise degree (\(Sur\)) by 2.69%, and preference mining degree (PMD) by 16.05%. Comparison with other evolutionary algorithms also shows that the combination of MPUSMs and its derived models with IMFEOA makes it easier to mine user's unknown preferences, and the search performance of IMFEOA is highly dependent on the underlying surrogate model. MPUSMs-IMFECOA also has certain limitations. In terms of surrogate models, their model parameters cannot be automatically adjusted. As shown in Experiment 1, the accuracy of the models shows a difference in performance on different datasets with the same parameters. Although they can provide sufficient recommendation accuracy, it still has impact on the final recommendation results. In addition, the classification method of the data of the training models may not fit the user' habits. At the algorithmic level, IMFEOA may unearth items that are not actually possible to exist, and thus the corresponding items found in the item pool using the shortest distance method are equally inaccurate. Therefore, the search area of IMFEOA needs to be adjusted. In the future, we hope to optimize three aspects under the framework of the MPUSMs-IMFECOA algorithm. The first is to apply other user surrogate models to the MPUSMs to address issues such as model accuracy, cold start, and inappropriate preference classification. Secondly, further improving the efficiency and effectiveness of knowledge transfer is a research direction. Finally, it is possible to study how to update the model more effectively during the interaction. Conceptualization, W.W., X.S. and L.B. ; Data curation, G.M. ; Formal analysis, W.W.; Investigation, W.W.; Methodology, W.W.; Resources, L.B. ; Software, W.W.; Supervision, X.S. ; Validation, G.M. and S.L. ; Visualization, S.L. ; Writing--original draft, W.W.; Writing--review and editing, X.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, Grant No. 61876184.