DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain Recommendation

Cross-domain recommendation (CDR) is a widely used methodology in recommender systems to combat data sparsity. It leverages user data across different domains or platforms for providing personalized recommendations. Traditional CDR assumes user preferences and behavior data can be shared freely among cloud and users, which is now impractical due to strict restrictions of data privacy. In this paper, we propose a Deployment-friendly Cloud-Device Collaboration framework for Cross-Domain Recommendation (De-CoCDR). It splits CDR into a two-stage recommendation model through cloud-device collaborations, i.e., item-recall on cloud and item re-ranking on device. This design enables effective CDR while preserving data privacy for both the cloud and the device. Extensive offline and online experiments are conducted to validate the effectiveness of DeCoCDR. In offline experiments, DeCoCDR outperformed the state-of-the-arts in three large datasets. While in real-world deployment, DeCoCDR improved the conversion rate by 45.3% compared with the baseline.