Uni- F ed R ec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021)

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.

## introduction online news platforms usually rely on personalized news recommendation techniques to help users obtain their interested news information @xcite . existing news recommendation models usually exploit users' historical behavior data to model user interests for matching candidate news @xcite @xcite @xcite . for example, @xcite employed a gru network to build user embeddings from browsed news. @xcite employed an attention network to build user embeddings by aggregating different clicked news. both of them match candidate news and user interests via the inner product of their embeddings. in short, most of these methods rely on centralized storage of user behavior data to train models and serve users. however, user behavior data is usually highly privacysensitive @xcite , and centrally storing them may arouse users' concerns on privacy leakage and violate some privacy protection regulations such as gdpr 1 . a few methods explore to recommend news in a privacy-preserving way @xcite . for instance, @xcite proposed to store user data in user clients and applied federated learning technique @xcite to train news recommendation models on decentralized data. in general, these methods usually focus on developing privacy-preserving model training approaches based on decentralized user behavior data for ranking candidate news. however, how to generate candidate news and serve users in a privacy-preserving way remains an open problem. in this paper, we propose a unified news recommendation framework based on federated learning (named uni-fedrec), which can utilize user behavior data locally stored in user clients to train models offline and serve users online in a privacy-preserving way. following a widely applied paradigm in real-world recommender systems @xcite , uni-fedrec contains a recall stage for personalized candidate news generation and a ranking stage for candidate news ranking. in the recall stage, the user client first locally learns multiple interest representations from clicked news to model diverse user interests. these representations are further uploaded to the server to recall a small number of candidate news (e.g., 100) from a large news pool. in the ranking stage, recalled candidate news are distributed to the user client and locally ranked for news personalized display. bedsides, user interest representations may encode user privacy information. @xcite . to protect private user information encoded in interest representations, we propose an interest decomposer-aggregator method with perturbation noise to synthesize interest representations with a group of basic interest embeddings. in addition, uni-fedrec utilizes user data decentralized in a large number of user clients to collaboratively train the recall and ranking model in a privacy-preserving way. extensive experiments on two real-world datasets verify that our method can significantly outperform baseline methods and effectively protect user privacy. in summary, our contributions are as follows: ## conclusion in this paper, we propose a unified privacypreserving news recommendation framework (uni-fedrec) that can utilize user data locally stored in user clients to train models and serve users. our framework contains a recall stage and a ranking stage. in the recall stage, the user client first employs a recall model to locally learn multiple interest representations from clicked news to model diverse user interest, which are further uploaded to the server to recall candidate news from a news pool. in the ranking stage, candidate news are distributed to the user client and locally ranked for personalized display. besides, we propose an interest decomposer-aggregator method with permutation noise to protect private user information encoded in interest representations. in addition, uni-fedrec collaboratively trains recall and ranking models on user data decentralized in massive user clients in a privacy-preserving way. experiments on two realworld datasets show that our method can significantly outperform baseline methods and effectively protect user privacy.