A Cross-Domain Recommendation Model Based on Asymmetric Vertical Federated Learning and Heterogeneous Representation

Cross-domain recommendation meets the personalized needs of users by integrating user preference features from different fields. However, the current cross-domain recommendation algorithm needs to be further strengthened in terms of privacy protection. This paper proposes a cross-domain recommendation model based on asymmetric vertical federated learning and heterogeneous representation. This model can improve the accuracy and diversity of recommendations under the premise of privacy protection. Firstly, we propose a privacy set intersection model based on data augmentation. This model improves the data imbalance among participants by introducing obfuscation sets. It can conceal the true data volumes of each party, thereby protecting the sensitive information of weaker parties. Secondly, we propose a heterogeneous representation method based on a walking strategy incorporating interaction timing. This method combines users' recent interests to generate node sequences that reflect the characteristics of user preferences. Then we use the Skip-Gram model to represent the node sequence in a low-dimensional embedding. Finally, we propose a cross-domain recommendation model based on vertical federated learning. This model uses the federated factorization machine to complete the interest prediction and protect the privacy data security of each domain. Experiments show that on the real data set, the model can further guarantee the data security of each participant in the asymmetric federated learning. It can also improve the recommendation accuracy on the target domain.