MetaEM: Meta Embedding Mapping for Federated Cross-domain Recommendation to Cold-Start Users

Abstract

Cross-domain recommendation exploits the rich data from source domain to solve the cold-start problem of target domain. Considering the recommendation system contains some user private information, how to provide accurate suggestions for cold-start users on the basis of protecting privacy is an important issue. Federated recommendation systems keep user private data on mobile devices to protect user privacy. However, compared to federated single-domain recommendation, federated cross-domain recommendation needs to train more models, making resource-constrained mobile devices infeasible to run large-scale models. In view of this, we design a meta embedding mapping method for federated cross-domain recommendation called MetaEM. The training stage of MetaEM includes pretraining and mapping. The pretrain stage learns user and item embeddings of source domain and target domain respectively. Items embeddings are divided into common and private. The common embeddings are shared by all users, and we train a meta-network to generate private embeddings for each user. The mapping stage learns to transfer user embeddings from source domain to target domain. In order to alleviate the negative impact of users with low number of ratings on mapping model, we employ a task-oriented optimization method. We implement the MetaEM prototype on large real-world datasets and extensive experiments demonstrate that MetaEM achieves the best performance and is more compatible with complicated models compared to other state-of-the-art baselines.

Supported by organization nudt.