## 1 Introduction  
In traditional recommendation algorithms [1], content-based, collaborative filtering-based and hybrid methods, it is difficult to extract user preference features effectively. As a result, deep learning has become a hot topic in the field of recommendation due to its powerful feature extraction [2]. Although deep learning effectively fuses multi-source heterogeneous data into recommendation systems [3; 4], there are few existing studies on dynamic recommendation, most of which only consider the accuracy of recommendation and lack diversity and novelty. In the realm of personalized recommendation [5; 6; 7; 8], the interactive evolutionary algorithms (IEAs) based on the user surrogate model (USM) are a highly effective search paradigm. The information obtained from interaction with users allows it to effectively track dynamic changes in user preferences. Due to its good performance in dynamic recommendation, it has been applied to scenarios such as movie recommendation [6], personalized pattern designs [8], preference guides [9], and energy system designs [10]. However, studiesrelated to IEAs still consider the diversity and novelty of recommendations and the use of deep learning methods in the construction of user surrogate models less. To increase the diversity and novelty of recommendations, items with new combinations of attributes can be obtained by fully exploiting the characteristics of the items themselves. Mining new items for a preference by transfer knowledge from other related preferences is an effective approach. The search process of IEAs is guided by a USM. Therefore, unless the USM can describe preference information from multiple perspectives, IEAs cannot effectively mine the implicit information between different preferences. Currently, it is common to build several models to solve the problem of multiple perspectives [11; 12; 13]. For example, using multiple local surrogate models, [PERSON] et al. [11] proposed an evolutionary algorithm that approaches the optimal solution of the original problem from different directions. Although surrogate models have been able to describe the problem from various perspectives, previous studies have not considered the interaction and sharing of information between models. Although there are strategies, subspace alignment [14], and feature mapping [15] to achieve feature transfer, there is no doubt that high computation will affect the timeliness required for personalized recommendation. Transferring genetic information between individuals of population is a viable way to address knowledge sharing between USMs. A multifactorial evolutionary algorithm (MFEA) [16] is a recently emerged approach for multi-task search. In MFEA, the evolutionary search is performed on multiple optimization problems or multiple search spaces simultaneously to improve evolutionary search capabilities by exploiting the associative knowledge between the tasks. In many successful cases of multi-task optimization scenarios, MFEA has demonstrated remarkable solution quality and search efficiency over traditional evolutionary algorithms [17; 18; 19]. To the best of our knowledge, the MFEA has not been extended to the IEAs-based personalized recommendation area. As we mentioned before, IEAs with only one surrogate cannot fully explore user preference information to provide diverse and novel items. Further consideration should be given to USMs describing user preferences from different perspectives, which will be searched as multiple optimization tasks, simultaneously. Keeping these in mind, here we propose a new IEA for enhancing diversity and novelty of personalized recommendations. Firstly, deep learning-based multidimensional preference surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are developed, all of which are composed of multiple models representing different dimensions of preferences and serving the population evolution process.