Liu, Weiming, et al. 2024. "Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation." Forty-first International Conference on Machine Learning.
Abstract:
Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information
(e.g., rating and review on overlapped users
or items) are accessible across domains. However,these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority - Digital Trust Centre - Trust Tech Funding Initiative
Grant Reference no. : IHPC/CFAR/G23-012
This research / project is supported by the National Key Rescarch and Development Program of China - NA
Grant Reference no. : 2022YFF0902001