Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie


Abstract
News recommendation techniques can help users on news platforms obtain their preferred news information. Most existing news recommendation methods rely on centrally stored user behavior data to train models and serve users. However, user data is usually highly privacy-sensitive, and centrally storing them in the news platform may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains a stage for candidate news generation (i.e., recall) and a stage for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.
Anthology ID:
2021.findings-emnlp.124
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1438–1448
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.124
DOI:
10.18653/v1/2021.findings-emnlp.124
Bibkey:
Cite (ACL):
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie. 2021. Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1438–1448, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (Qi et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.124.pdf
Data
MIND