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Register a new userUni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
UNI-FEDREC: إطار توصية أخبار محافظة الخصوصية الموحدة للتدريب النموذجي والخدمة عبر الإنترنت
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يمكن أن تساعد تقنيات توصيات الأخبار المستخدمين على منصات الأخبار للحصول على معلومات الأخبار المفضلة لديهم. تعتمد معظم طرق توصيات الأخبار الحالية على بيانات سلوك المستخدم المخزنة مركزيا لتدريب نماذج وخدمة المستخدمين. ومع ذلك، فإن بيانات المستخدم عادة ما تكون حساسة خصوصية عالية، وتخزينها مركزيا في منصة الأخبار قد تثير مخاوف الخصوصية والمخاطر. في هذه الورقة، نقترح إطار توصية أخبار موحدة، والتي يمكن أن تستخدم بيانات المستخدم المخزنة محليا في عملاء المستخدمين لتدريب النماذج وخدمة المستخدمين بطريقة محافظة الخصوصية. بعد النموذج المستخدمة على نطاق واسع في أنظمة التوصية في العالم الحقيقي، يحتوي إطار عملنا على مرحلة للجيل الأخبار المرشح (I.E.، استدعاء) ومرحلة لترتيب الأخبار المرشح (أي، الترتيب). في مرحلة الاستدعاء، يتعلم كل عميل محليا تمثيلات فائدة متعددة من الأخبار النقر باهتمامات المستخدم النموذجية الشاملة. تم تحميل هذه التمثيلات إلى الخادم لاستدعاء أخبار المرشحين من تجمع أخبار كبير، والتي يتم توزيعها بشكل إضافي على عميل المستخدم في مرحلة الترتيب لعرض الأخبار المخصص. بالإضافة إلى ذلك، نقترح طريقة Decomposer-Decomposer-Decomposer مع ضوضاء الاضطرابات لتحسين حماية معلومات المستخدم الخاصة المشفرة في تمثيلات اهتمام المستخدم. علاوة على ذلك، فإننا نتدرب بشكل تعاوني في تذكر نماذج الترتيب والترتيب على البيانات اللامركزية في عدد كبير من عملاء المستخدمين بطريقة الحفاظ على الخصوصية. تبين التجارب في مجموعات بيانات الأخبار الحقيقية في العالم أن طريقتنا يمكن أن تفوق أساليب خط الأساس وتحمي خصوصية المستخدم بشكل فعال.
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.
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