# MFCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
## 2 Related Work
### Cross-Domain Recommendation  
CDR leverages greater source domain information to increase recommendation performance in sparse domains in order to tackle the long-standing data sparsity problem in recommendation systems. The cold-start user recommendation workflow across domains is shown in Figure 1, where the model learns features for overlapping users in the source and target domains to obtain a mapping of user preferences and then makes recommendations in the target domain based on the mapping of user preferences. CDR jointly mines information from two datasets to better construct their relevance and present more meaningful recommendation results. Cross-domain recommendation systems can be classified into two broad categories based on the type of method: mapping-based cross-domain recommendation techniques and multi-domain recommendation algorithms trained cooperatively. In the collaborative multi-domain cross-domain recommendation technique CMF [16], for instance, data from the source and target domains are combined for matrix decomposition, and knowledge transfer is achieved by exchanging intermediate vectors, but the hidden vectors it decomposes are all consistent; therefore, it cannot achieve efficient knowledge transfer. CoNet [17] considers cross-domain recommendation in a neural network by introducing a cross-connection to achieve dual knowledge transfer; however, a substantial quantity of data is necessary to train the network, and the quality of the suggestions is contingent on the historical datasets. HYPNER [18] improves the quality of recommendations by combining collaborative filtering-based and content-based filtering methods.  
Learning user preferences across domains are the topic of a second set of CDR approaches. In the mapping-based cross-domain recommendation approach, CST [19] migrates user and item knowledge into the auxiliary data and migrates the principal coordinates of people and things discovered in the auxiliary data matrix into the target domain; however, it consumes more memory and is slower to compute. OCA [20] addresses the effects of data sparsity through ordered clustering. EMCDR [21] determines that the same user representation in multiple domains or the same object representation are related; hence, it represents this relationship using a display mapping function but amplifies the sparsity of the data when learning the linkage. In real life, where only a small percentage of users or items are shared between two domains, SSCDR [22] proposes a semi-supervised approach that uses data from non-shared users to learn the mapping function; however, semi-supervised methods often require training the network from scratch, which can be time-consuming. Some approaches explicitly model user preferences. CATN [23] learns cross-domain layer correlations by extracting multiple layers for each user and item from the review document and using an attention mechanism, but it cannot capture location information and cannot learn sequential relationships in a sequence. EnsVAE [24] uses embedding-based representations and stacked ensemble learning techniques to reduce user preference for items that are often rated by users. [PERSON] et al. [25] consider the issue of user privacy in the context of cross-domain. [PERSON] et al. [26] proposed an ensemble variational autoencoder framework for recommendation to improve the accuracy of recommendation systems. Because user preferences are personalized, [PERSON] et al. [27] propose to use meta-networks to model personalized user preferences. We are inspired by this to model user preferences in the model.  
However, the CDR methods mentioned above, which require data to be stored on a central server, do not take privacy protection into account, and there is a risk of attack in their uploading of user behavioral data to the central server. Users' behavioral data are sensitive, and doing this poses a significant risk of a privacy breach.