Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.

翻译：本研究提出了一种多层面方法，通过联邦学习框架增强社交媒体平台的用户交互与内容相关性。我们引入了个性化GPT和基于上下文的社交媒体LLM模型，利用联邦学习保障隐私与安全。四个客户端实体接收基础GPT-2模型及本地收集的社交媒体数据，通过联邦聚合确保模型保持最新状态。后续模块专注于用户帖子分类、计算用户画像分数，以及从好友列表中识别相关帖子。量化社交参与度的方法结合矩阵分解技术，实现了实时个性化内容推荐。自适应反馈循环与可读性评分算法进一步提升了向用户展示内容的质量与相关性。本系统为内容过滤与推荐提供了全面解决方案，在保护用户隐私的同时，营造了定制化且富有吸引力的社交媒体体验。