News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models offline and provide online recommendation services. However, user data is usually highly privacy-sensitive, and centrally storing them 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 two stages. The first one is for candidate news generation (i.e., recall) and the second one is 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.
翻译:对个人化在线新闻服务来说,新闻建议很重要。大多数现有新闻建议方法都依靠集中储存的用户行为数据来培训离线模型和提供在线建议服务。然而,用户数据通常对隐私问题敏感,集中储存可能会引起隐私关切和风险。在本文中,我们提出了一个统一的新闻建议框架,可以使用当地存储在用户客户中的用户数据来培训模型,并以隐私保护方式为用户服务。在现实世界建议系统广泛使用的范例中,我们的框架包含两个阶段。第一个阶段是候选人新闻生成(即,回顾),第二个阶段是候选新闻排序(即,排名),第二个阶段是候选新闻排序(即,排名)。在回顾阶段,每个客户都从点击新闻到全面模型用户兴趣中学习多种利益代表。我们把这些代表上传到服务器,以便从大型新闻库中检索候选新闻,然后在个人化新闻显示的排名阶段,再向用户用户用户用户用户用户隐私显示的排序。此外,我们建议一种在用户利益展示中有效地在用户保密性基准模型上对用户隐私进行分级排序,在用户隐私模式上以两种方式对用户隐私进行真正的数据库进行检索。